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Mete Ozay

Mete Ozay has 2 papers in our library, in 2026, most of them on Named entity recognition, Language models & pretraining.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 19 · 137 works · 2,186 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11214

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

🎓Middle East Technical UniversityAnkara · Türkiye2008–2023 · 26 papers🏢SamsungChertsey · United Kingdom2021–2025 · 21 papers🎓Tohoku UniversitySendai · Japan2015–2019 · 20 papers🎓Princeton UniversityPrinceton · United States2012–2022 · 7 papers🎓University of BirminghamBirmingham · United Kingdom2013–2015 · 7 papers🎓University of OxfordOxford · United Kingdom2024 · 2 papers🎓Air Force Engineering UniversityXi'an · China2019–2022 · 2 papers🎓Poznań University of TechnologyPoznan · Poland2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workNamed entity recognition · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. K-Merge: Online Continual Merging of Adapters for On-device Large Language Models (2026 · 2 citations)
    On-device LLMs often depend on task-specific LoRA adapters, but phones have limited storage. New adapters also tend to arrive one at a time as users ask for new features. The authors formalize this as online continual merging . A device may keep at most K adapters. Each incoming single-task LoRA must be either placed…
  2. Decoding Text Spans for Efficient and Accurate Named-Entity Recognition (2026)
    Span-based named-entity recognition, especially the PL-Marker family, tends to beat plain token tagging on accuracy. Its cost is high, though: it enumerates many candidate spans and pushes marker tokens through every encoder layer. That makes it expensive for high-volume or on-device serving.

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 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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