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Mujeen Sung

Mujeen Sung has 2 papers in our library, in 2021, most of them on Language models & pretraining, Knowledge graphs & ontologies; often with Jaewoo Kang, Jinhyuk Lee, Danqi Chen.

2 papers in our library · 2021

OpenAlex · ORCID · Wikidata · h-index 11 · 35 works · 861 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 152 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4411

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

🎓Kyung Hee UniversitySeoul · South Korea2024–2026 · 11 papersEmployment since 2024current🎓Korea UniversitySeoul · South Korea2019–2025 · 16 papersEducation until 2015🎓Princeton UniversityPrinceton · United States2020–2023 · 3 papers🏢AmazonSeattle · United States2023 · 2 papers

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

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 work

🧭 Topics 2Research subjects and languages the work is about.Knowledge graphs & ontologies · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20212 papersMujeen Sung's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Can Language Models be Biomedical Knowledge Bases? (2021 · 127 citations)
    This work asks whether pre-trained language models can act as knowledge bases for expert biomedical facts, rather than the general trivia tested by LAMA.
  2. Learning Dense Representations of Phrases at Scale (2021 · 126 citations)
    DensePhrases tackles open-domain question answering as retrieval over a pre-built index of phrases. It does not run a reader model over retrieved documents at query time. Earlier phrase-retrieval systems (DenSPI, DenSPI + Sparc) relied on sparse vectors and still trailed retriever-reader pipelines. The authors ask…

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. Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models ↗arXiv:2401.15269 · no summary yetMinbyul Jeong et al.2024 · 164 citations
  2. Can Language Models be Biomedical Knowledge Bases?Conference on Empirical Methods in Natural Language Processing (EMNLP)Mujeen Sung, Jinhyuk Lee, Sean Yi, et al.2021 · 127 citations
  3. Learning Dense Representations of Phrases at ScaleAnnual Meeting of the Association for Computational Linguistics (ACL)Jinhyuk Lee, Mujeen Sung, Jaewoo Kang, et al.2021 · 126 citations

 Co-authors here

4 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 152 times (of the 141,840 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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