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Kiril Gashteovski

Kiril Gashteovski has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Carolin Lawrence, Graham Neubig, Vijay Viswanathan.

3 papers in our library · 2024–2026

OpenAlex · h-index 10 · 79 works · 477 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 128 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #6271

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

🏢NECHeidelberg · Germany2020–2026 · 23 paperscurrent🎓Ss. Cyril and Methodius University in SkopjeSkopje · North Macedonia2023–2026 · 20 paperscurrent🎓University of MannheimMannheim · Germany2017–2021 · 7 papers🏢NECTokyo · Japan2022–2024 · 3 papers🎓University of WashingtonSeattle · United States2024–2025 · 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksGeneral Social Sciences · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models Enable Few-Shot Clustering (2024 · 111 citations)
    Clustering without supervision is underspecified, because the algorithm cannot know which grouping the user actually wants.
  2. Evaluating Language Models as Synthetic Data Generators (2025 · 32 citations)
    Synthetic data now drives much of language model post-training. Earlier data-generation studies, however, each used different generator LMs, data volumes, student models and benchmarks, so their results cannot be compared to show which LM produces the most useful training data. Kim et al. address this with…
  3. Compositional Steering of Large Language Models with Steering Tokens (2026)
    Practical LLM deployments often need outputs that satisfy several requirements at once, such as a target language, length and format.

All works

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

  1. MinIE: Minimizing Facts in Open Information Extraction ↗Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · no summary yetKiril Gashteovski, Rainer Gemulla, Luciano del Corro2017 · 197 citations
  2. Large Language Models Enable Few-Shot ClusteringTransactions of the Association for Computational Linguistics (TACL)Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, et al.2024 · 111 citations
  3. Evaluating Language Models as Synthetic Data GeneratorsAnnual Meeting of the Association for Computational Linguistics (ACL)Seungone Kim, Juyoung Suk, Xiang Yue, et al.2025 · 32 citations
  4. Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetEmmy Liu et al.2025 · 6 citations
  5. KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yetHaris Widjaja et al.2022 · 6 citations
  6. On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists ↗arXiv:2605.20668 · no summary yetSeungone Kim et al.2026 · 2 citations
  7. Compositional Steering of Large Language Models with Steering TokensAnnual Meeting of the Association for Computational Linguistics (ACL)Gorjan Radevski, Kiril Gashteovski, Giwon Hong, et al.2026
  8. Scaling Evaluation-Time Compute with Reasoning Models as Evaluators ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetSeungone Kim et al.2026
  9. TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action ↗Findings of the Association for Computational Linguistics: EACL 2026 · no summary yetChenyue Zhou et al.2026

 Co-authors here

11 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 128 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

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