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Yunsu Kim

Yunsu Kim has 2 papers in our library, in 2019–2023, most of them on Machine translation, Language models & pretraining; often with Hermann Ney.

2 papers in our library · 2019–2023

OpenAlex · ORCID · h-index 11 · 55 works · 507 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 133 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4664

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

🎓Sungkyunkwan UniversitySuwon · South Korea2024–2025 · 6 paperscurrent🏥Samsung Medical CenterSeoul · South Korea2025 · 4 paperscurrent🏥Inje University Ilsan Paik HospitalGoyang-si · South Korea2024–2025 · 2 paperscurrent🎓RWTH Aachen UniversityAachen · Germany2013–2020 · 19 papers🎓Pohang University of Science and TechnologyPohang · South Korea2022–2024 · 6 papers🏢UpStageYongin-si · South Korea2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

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

🧭 Topics 2Research subjects and languages the work is about.Machine translation · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paperSOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

2010s

20191 paperWhen and Why is Document-level Context Useful in Neural Machine Translation?

 Papers

The most cited first (OpenAlex and our library).

  1. When and Why is Document-level Context Useful in Neural Machine Translation? (2019 · 124 citations)
    This paper asks whether the gains reported for document-level neural machine translation really come from using discourse context.
  2. SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling (2023 · 75 citations)
    Scaling up LLMs usually means training ever-larger networks or adopting mixture-of-experts designs, which complicate training and inference pipelines.

All works

3 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. When and Why is Document-level Context Useful in Neural Machine Translation?Workshop on Discourse in Machine TranslationYunsu Kim, Duc Thanh Tran, Hermann Ney2019 · 124 citations
  2. SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-ScalingarXiv:2312.15166Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.2023 · 75 citations
  3. Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine TranslationsProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · original ↗ · no summary yetBaikjin Jung et al.2023 · 2 citations

 Co-authors here

1 co-author 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 133 times (of the 141,840 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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