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Sang Yun Kwon

Sang Yun Kwon has 2 papers in our library, in 2024–2025, most of them on Semitic languages, Language models & pretraining, Speech & spoken language; often with Muhammad Abdul-Mageed, Alcides Alcoba Inciarte, El Moatez Billah Nagoudi.

2 papers in our library · 2024–2025

OpenAlex · h-index 3 · 10 works · 15 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 21 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9030

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 British ColumbiaVancouver · Canada2023 · 3 papers

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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksEducation · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Semitic languages · 2 worksLanguage models & pretraining · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking (2025 · 18 citations)
    Many LLMs still lean toward Anglo-American culture and handle English better than other languages. This makes culturally loaded figurative language, such as proverbs, especially hard for them. The authors present Jawaher, a manually curated benchmark of Arabic proverbs from 20 Arabic varieties spread across the Arab…
  2. On the Utility of Pretraining Language Models on Synthetic Data (2024 · 3 citations)
    Pretraining language models normally needs very large amounts of clean text, which low-resource languages often lack. The authors test whether Arabic models can be pretrained mostly or entirely on machine-produced text. They build four synthetic corpora:

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. JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM BenchmarkingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, et al.2025 · 18 citations
  2. Gazelle: An Instruction Dataset for Arabic Writing Assistance ↗Findings of the Association for Computational Linguistics: EMNLP 2024 · no summary yetSamar Mohamed Magdy et al.2024 · 7 citations
  3. On the Utility of Pretraining Language Models on Synthetic DataArabic Natural Language Processing Conference (ArabicNLP)Alcides Alcoba Inciarte, Sang Yun Kwon, El Moatez Billah Nagoudi, et al.2024 · 3 citations

 Co-authors here

7 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.

 Cited by

Papers of our library cite their works 21 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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