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Jaewook Kang

Jaewook Kang has 3 papers in our library, in 2021–2025, most of them on Language models & pretraining, Text generation; often with Kang Min Yoo, Donghyun Kwak, Najoung Kim.

3 papers in our library · 2021–2025

OpenAlex · ORCID · h-index 6 · 27 works · 306 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 184 times in our library · ⭐ Paper Stars ranking: #4631

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

🎓Gwangju Institute of Science and TechnologyGwangju · South Korea2012–2014 · 5 papers🏢NaverSeongnam-si · South Korea2024–2025 · 2 papers🏢NAVER CloudSeongnam-si · South Korea2021–2024 · 2 papers🎓Sahmyook UniversitySeoul · South Korea2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksText generation · 1 work

 Works by year

2020s

20251 paper20241 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021 · 270 citations)
    Prompting very large language models like GPT-3 can solve classification tasks from a few examples, but it has limits.
  2. HyperCLOVA X Technical Report (2024 · 44 citations)
    Most large language models are built mainly for English and carry the cultural assumptions of English-speaking, especially North American, data.
  3. CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists (2025 · 37 citations)
    LLM-based judges for generated text often disagree with one another and produce unstable scores. The authors attribute this to vague quality criteria combined with holistic 1–5 Likert ratings. CheckEval replaces such ratings with a checklist of yes/no questions. First, humans choose quality dimensions (e.g.…

 Co-authors here

5 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 184 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated

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