--- title: "Hangyeol Yoo — author profile and papers — Elamur Research" description: "Hangyeol Yoo, Seoul National University of Science and Technology: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/hangyeol-yoo/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hangyeol-yoo/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Hangyeol Yoo # Hangyeol Yoo Hangyeol Yoo has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/), [Tokenization & segmentation](https://md.research.elamur.ai/free/en/topics/tokenization/); often with [Inho Won](https://md.research.elamur.ai/free/en/authors/inho-won/), [KyungTae Lim](https://md.research.elamur.ai/free/en/authors/kyungtae-lim/), [Cheoneum Park](https://md.research.elamur.ai/free/en/authors/cheoneum-park/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5106072254) · [**h-index 1**](https://openalex.org/authors/A5106072254) · [11 works](https://openalex.org/works?filter=author.id:A5106072254) · [3 citations](https://openalex.org/works?filter=author.id:A5106072254&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/hangyeol-yoo/#papers) · [⭐ Paper Stars ranking: #13588](https://md.research.elamur.ai/free/en/paper-stars/page/136/#star-13588) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Hangyeol+Yoo) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hangyeol+Yoo) · [dblp](https://dblp.org/search?q=Hangyeol+Yoo) · [ACL Anthology](https://aclanthology.org/search/?q=Hangyeol+Yoo) ## Affiliations [🎓**Seoul National University of Science and Technology** Seoul · South Korea 2024–2026 · 5 papers*current*](https://md.research.elamur.ai/free/en/universities/seoul-national-university-of-science-and-technology/) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Tokenization & segmentation · 1 work](https://md.research.elamur.ai/free/en/topics/tokenization/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/hangyeol-yoo/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/yoo-2026-elo-efficient-layer-specific-optimization/)** *(2026)* Continual pretraining (CP) of multilingual LLMs on a new language is costly and often weakens English ability. 2. **[TReX: Tokenizer Regression for Optimal Data Mixture](https://md.research.elamur.ai/free/en/papers/won-2026-trex-tokenizer-regression-optimal-data/)** *(2026)* Multilingual tokenizers compress text unevenly depending on how much data each language contributes. The usual ways to set these language proportions are hand-tuned guesses or expensive repeated full-scale training runs. TReX instead treats mixture selection as a prediction problem. The authors draw many candidate… ## Co-authors here 5 co-authors with a page, the most papers together first. - [Inho Won](https://md.research.elamur.ai/free/en/authors/inho-won/) ×2 - [KyungTae Lim](https://md.research.elamur.ai/free/en/authors/kyungtae-lim/) ×2 - [Cheoneum Park](https://md.research.elamur.ai/free/en/authors/cheoneum-park/) - [Hoyun Song](https://md.research.elamur.ai/free/en/authors/hoyun-song/) - [Minjun Kim](https://md.research.elamur.ai/free/en/authors/minjun-kim/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/) ×2: [EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/) ×2 Updated 2 November 2026