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Koen Oostermeijer

Koen Oostermeijer has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Andrew Dai, Alex Havrilla, Alon Albalak.

2 papers in our library · 2023–2024

OpenAlex · h-index 5 · 11 works · 44 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 25 times in our library · ⭐ Paper Stars ranking: #10210

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

🏢AlphacoreTempe · United States2026 · 1 papercurrent🎓University of Science and Technology of ChinaHefei · China2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 · 2 worksComputer Science · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workGenetics · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Quality-Diversity through AI Feedback (2023 · 19 citations)
    Quality-diversity (QD) search maintains a population of solutions that are both good and varied. However, it has rarely been used for creative text because quality and diversity are hard to define in code. The authors propose QDAIF, an evolutionary method in which language models handle all three parts of QD search.…
  2. Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models (2024 · 17 citations)
    Synthetic data produced by LLMs is now a common way to expand training sets. The methods that create it differ in seed data, generator models, prompts, filters and task-specific tricks, so it is hard to see which design choices actually help. This survey suggests judging generation algorithms by the properties of the…

 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 25 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

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