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Minbyul Jeong

Minbyul Jeong has 2 papers in our library, in 2024, most of them on Question answering, Reasoning, Information retrieval & search; often with Jaewoo Kang, Chanwoong Yoon, Hyeon Hwang.

2 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 12 · 37 works · 1,515 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 58 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5705

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

🏢UpStageYongin-si · South Korea2024–2026 · 9 papersEmployment since 2024current🎓Korea UniversitySeoul · South Korea2015–2024 · 19 papersEducation 2015–2019Education 2019–2024🎓Korea UniversityTokyo · Japan2019–2022 · 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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksMedicine · 1 workHealth Informatics · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Information retrieval & search · 1 workReasoning · 1 workEfficient models & compression · 1 workQuestion answering · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models (2024 · 164 citations)
    General-purpose retrieval-augmented LLMs often fail in medicine. Methods tuned on open-domain data, such as Self-RAG, tend to pull irrelevant documents and misjudge their usefulness on clinical questions. The authors address this with Self-BioRAG, a biomedical adaptation of Self-RAG. The model decides for itself…
  2. CompAct: Compressing Retrieved Documents Actively for Question Answering (2024 · 34 citations)
    Retrieval-augmented QA systems often give readers long, noisy contexts. Useful evidence may sit in lower-ranked documents, and multi-hop questions need facts joined across several sources. Existing query-focused compressors work in a single pass, so they tend to drop such evidence. CompAct addresses this by splitting…

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. Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language ModelsBioinformaticsMinbyul Jeong, Jiwoong Sohn, Mujeen Sung, et al.2024 · 164 citations
  2. CompAct: Compressing Retrieved Documents Actively for Question AnsweringConference on Empirical Methods in Natural Language Processing (EMNLP)Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, et al.2024 · 34 citations
  3. Solar Open Technical Report ↗arXiv (Cornell University) · no summary yetSungrae Park et al.2026 · 3 citations
  4. Solar Open 2 Technical Report ↗arXiv (Cornell University) · no summary yetSungrae Park et al.2026

 Co-authors here

6 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 58 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

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