Elamur Research⌕
☰
home / authors / Young Jin Kim

Young Jin Kim

Young Jin Kim has 2 papers in our library, in 2024, most of them on Machine translation; often with Amr Sharaf, Benjamin Van Durme, Chen Liang.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 11 · 25 works · 499 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 532 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5531

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

🎓Ewha Womans UniversitySeoul · South Korea2025–2026 · 3 paperscurrent🏢MicrosoftRedmond · United States2018–2023 · 9 papersEmployment since 2018 · Principal Researcher🎓Johns Hopkins UniversityBaltimore · United States2023–2024 · 2 papers🎓University of British ColumbiaVancouver · Canada2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation (2024 · 212 citations)
    Mid-sized LLMs (7B/13B) translate reasonably well but still trail GPT-4 and the strongest WMT systems. The authors trace part of this gap to supervised fine-tuning (SFT). SFT copies reference translations, so the model can only be as good as those references. It also never teaches the model to avoid output that is…
  2. GRIN: GRadient-INformed MoE (2024 · 7 citations)
    Mixture-of-Experts (MoE) models activate only a few expert subnetworks per token. However, the discrete routing step has no true gradient, so standard training substitutes the gating gradient. Large-scale MoE training also usually relies on expert parallelism, which forces capacity limits and discards overflow…

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. How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation ↗arXiv:2302.09210 · no summary yetAmr Hendy et al.2023 · 525 citations
  2. Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine TranslationarXiv:2401.08417Haoran Xu, Amr Sharaf, Yunmo Chen, et al.2024 · 212 citations
  3. GRIN: GRadient-INformed MoEarXiv:2409.12136Liyuan Liu, Young Jin Kim, Shuohang Wang, et al.2024 · 7 citations

 Co-authors here

12 of 18 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 532 times (of the 149,439 papers whose bibliographies we read); 96 citing papers with a page here →.

Updated

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