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Branislav Pecher

Branislav Pecher has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Low-resource languages; often with Ivan Srba, Maria Bielikova, Jakub Simko.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 7 · 35 works · 208 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 20 times in our library · ⭐ Paper Stars ranking: #8094

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 · 26 papersEmployment since 2020current🎓Brno University of TechnologyBrno · Czechia2022–2026 · 11 papersEmploymentcurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksLow-resource languages · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance (2025 · 24 citations)
    The paper asks how much labelled data a small, task-specialised language model needs before it beats a large general-purpose model used without parameter updates.
  2. Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026 · 15 citations)
    Picking which labelled examples to use strongly affects few-shot performance. Methods for large language models tend to rely on selectors built for specific in-context learning setups and overlook classic strategies from supervised learning, such as active learning and core-set methods. The authors propose ACSESS…
  3. 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.

 Co-authors here

5 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 20 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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