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Jan Cegin

Jan Cegin has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Low-resource languages, Multilingual & cross-lingual NLP; often with Jakub Simko, Branislav Pecher, Ivan Srba.

2 papers in our library · 2025–2026

OpenAlex · h-index 4 · 29 works · 64 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 37 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9696

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🤝Kempelen Institute of Intelligent TechnologiesBratislava · Slovakia2020–2026 · 17 paperscurrent🎓Brno University of TechnologyBrno · Czechia2023–2025 · 7 papers🎓Slovak University of Technology in BratislavaBratislava · Slovakia2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksLow-resource languages · 2 worksEvaluation & benchmarks · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025 · 11 citations)
    LLM-generated synthetic data is often used to train compact task models, but it is unclear which prompting strategies work best outside English, especially for under-resourced languages.
  2. Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026 · 1 citation)
    The paper asks whether a large multilingual LLM is more useful for low-resource classification as a classifier or as a source of training data for smaller models.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetJan Cegin, Jakub Simko, Peter Brusilovsky2023 · 56 citations
  2. A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Tatiana Anikina, Jan Cegin, Jakub Simko, et al.2025 · 11 citations
  3. Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual ClassificationFindings of the Association for Computational LinguisticsBranislav Pecher, Jan Cegin, Robert Belanec, et al.2026 · 1 citation

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 37 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jan-cegin/