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Sangkeun Jung

Sangkeun Jung has 2 papers in our library, in 2025–2026, most of them on English.

2 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 14 · 92 works · 870 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #11256

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

🎓Chungnam National UniversityDaejeon · South Korea2018–2026 · 44 paperscurrent🎓Pohang University of Science and TechnologyPohang · South Korea2006–2010 · 16 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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workApplied Mathematics · 1 workComputer Vision and Pattern Recognition · 1 workMathematics · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring (2025 · 2 citations)
    AI tutors for English need teacher feedback data that is both plentiful and good. Collecting and ranking it by hand is slow and expensive. The authors present FEAT, a framework that uses LLMs to build preference data for this feedback, and they release three datasets. DIRECT-Manual (DM) extends DIRECT: five feedback…
  2. When Does Auxiliary Modality Matter in Solving Geometric Problems? A Comprehensive Study of Textual, Formal, and Visual Modalities (2026)
    Geometry problems require a model to connect what the text says with what the diagram shows, and multimodal LLMs often misread diagrams.

All works

4 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. FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI TutoringAnnual Meeting of the Association for Computational Linguistics (ACL)Hyein Seo, Taewook Hwang, Yohan Lee, et al.2025 · 2 citations
  2. An Analysis of the Impact of Problem Paraphrasing on LLM-Based Mathematical Problem Solving ↗Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics · no summary yetYerim Han et al.2025 · 1 citation
  3. When Does Auxiliary Modality Matter in Solving Geometric Problems? A Comprehensive Study of Textual, Formal, and Visual ModalitiesConference of the European Chapter of the Association for Computational Linguistics (EACL)Hyuk Namgoong, Jeesu Jung, Yerim Han, et al.2026
  4. AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetHyuk Namgoong et al.2025

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 2 times (of the 149,439 papers whose bibliographies we read).

Updated

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