🧑‍🔬 Kyomin Jung

🏛️ Affiliations

🎓Seoul National UniversitySeoul · South Korea2012–2026 · 197 papersEmployment since 2013 · Electrical and Computer Engineeringcurrent🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2009–2014 · 18 papers🎓Massachusetts Institute of TechnologyCambridge · United States2005–2010 · 15 papers🏢LG AI ResearchSeoul · South Korea2023–2024 · 5 papers🏢GoogleMountain View · United States2026 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation (2026 · 5 citations)
   LLMs are increasingly used to judge whether generated code is correct without reference solutions or tests.
2. A Universal Avoidance Method for Diverse Multi-branch Generation (2026)
   Sampling a generative model several times from the same prompt often yields near-duplicate results. This is a weakness for creative work such as story writing or illustration, which needs multi-branch diversity. Earlier fixes either operate per token or per pixel, which is costly, or depend on a specific architecture.

All works

5 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. Improving Neural Question Generation Using Answer Separation ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetYanghoon Kim et al.2019 · 224 citations
2. Comparative Studies of Detecting Abusive Language on Twitter ↗Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) · no summary yetYounghun Lee, Seunghyun Yoon, Kyomin Jung2018 · 114 citations
3. Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code EvaluationFindings of the Association for Computational LinguisticsJiwon Moon, Yerin Hwang, Dongryeol Lee, et al.2026 · 5 citations
4. A Universal Avoidance Method for Diverse Multi-branch GenerationFindings of the Association for Computational LinguisticsKyeongman Park, Minha Jhang, Kyomin Jung2026
5. Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset ↗arXiv:2605.29365 · no summary yetHyojeong Yu; Hyukhun Koh; Minsung Kim;, Kyomin Jung2026

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
• Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
• Proceedings of the AAAI Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 75 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated 1 November 2026

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