Elamur Research⌕
☰
home / authors / Samet Oymak

Samet Oymak

Samet Oymak has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Dimitris Papailiopoulos, Jaewoong Cho, Kangwook Lee.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 24 · 164 works · 2,805 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #9808

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

🎓University of MichiganAnn Arbor · United States2021–2026 · 37 paperscurrent🏢GoogleMountain View · United States2017–2026 · 4 paperscurrent🎓University of California, RiversideRiverside · United States2017–2024 · 51 papers🎓California Institute of TechnologyPasadena · United States2011–2017 · 26 papers🎓University of California, BerkeleyBerkeley · United States2013–2018 · 8 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks (2024 · 41 citations)
    In-context learning (ICL) has mostly been studied in Transformers. This paper asks whether attention-free state-space models (SSMs), chiefly Mamba and also S4, can acquire the same ability. Large-scale ICL is hard to test cheaply, so the authors follow the Garg et al. (2022) setup: small models are trained from…
  2. Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement (2025 · 6 citations)
    Small language models distilled from strong reasoners through supervised fine-tuning (SFT) gain reasoning ability, but they tend to waste tokens.

 Co-authors here

5 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 37 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/samet-oymak/