Elamur Research⌕
☰
home / authors / Minjun Kim

Minjun Kim

Minjun Kim has 2 papers in our library, in 2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Efficient models & compression; often with Cheoneum Park, Hangyeol Yoo, Inho Won.

2 papers in our library · 2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13060

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

🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2026 · 2 paperscurrent🎓Seoul National University of Science and TechnologySeoul · South Korea2026 · 1 papercurrent🎓Chungbuk National UniversityCheongju-si · South Koreacurrent🔬Korea Food Research InstituteWanju · South Koreacurrent

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 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 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEfficient models & compression · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression (2026 · 1 citation)
    Large language models are expensive to store and run, and parameter sharing can reduce that memory cost. Existing sharing schemes, however, group layers by fixed positional patterns and rely on bulky LoRA-style adapters to repair the damage. The authors propose SharVeT, which works at the level of individual module…
  2. ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs (2026)
    Continual pretraining (CP) of multilingual LLMs on a new language is costly and often weakens English ability.

 Co-authors here

4 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.

Updated

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