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Mateusz Lango

Mateusz Lango has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Text generation, Evaluation & benchmarks; often with Ondřej Dušek, Patrícia Schmidtová, Simone Balloccu.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 10 · 82 works · 548 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 130 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5929

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

🎓Charles UniversityPrague · Czechia2018–2026 · 44 paperscurrent🎓Poznań University of TechnologyPoznan · Poland2016–2026 · 40 paperscurrent

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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workText generation · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs (2024 · 174 citations)
    The authors study how test data ends up in closed LLMs (data contamination). They separately examine a phenomenon they call indirect data leakage. When researchers evaluate GPT-3.5 and GPT-4 through ChatGPT, benchmark examples may become training material for subsequent versions of the model. Existing contamination…
  2. One-step Nonautoregressive Natural Language Generation with Shortcut Flow Matching Models (2026)
    Diffusion-style non-autoregressive (NAR) text generators can in principle decode all tokens in parallel. In practice they need many denoising iterations, and their quality collapses when the iteration count is cut drastically. Flow-matching models also lose much of their quality when restricted to one step. The…

All works

6 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. Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMsConference of the European Chapter of the Association for Computational Linguistics (EACL)Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.2024 · 174 citations
  2. A Survey of Text Style Transfer: Applications and Ethical Implications ↗arXiv:2407.16737 · no summary yetSourabrata Mukherjee et al.2024 · 6 citations
  3. With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector ↗Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems · no summary yetOndrej Platek, Mateusz Lango, Ondrej Dusek2023 · 3 citations
  4. LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetMateusz Lango, Ondrej Dusek2025 · 2 citations
  5. One-step Nonautoregressive Natural Language Generation with Shortcut Flow Matching ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jędrzej Warczyński, Ondrej Dusek, Mateusz Lango2026
  6. Last Translation Benchmark ↗arXiv:2609.04173 · no summary yetVilem Zouhar et al.2026

 Co-authors here

3 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 130 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

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