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Sang-goo Lee

Sang-goo Lee has 2 papers in our library, in 2021–2022; often with Kang Min Yoo, Taeuk Kim, Sang-Woo Lee.

2 papers in our library · 2021–2022

OpenAlex · ORCID · Wikidata · h-index 20 · 117 works · 1,532 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 178 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3991

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

🎓Seoul National UniversitySeoul · South Korea2002–2026 · 66 papersEducation until 1985Employmentcurrent🏢NAVER CloudSeongnam-si · South Korea2021–2023 · 3 papers🔬Oak Ridge National LaboratoryOak Ridge · United States2013–2014 · 3 papers🎓Hanyang UniversitySeoul · South Korea2021–2022 · 2 papers🏢NaverSeongnam-si · South Korea2021–2022 · 2 papers🎓Northwestern UniversityEvanston · United States1985–1990Education 1985–1987Education 1987–1990

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Concepts, shelves and topics

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

 Works by year

2020s

20221 paperSang-goo Lee's papers of 202220211 paperSang-goo Lee's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Self-Guided Contrastive Learning for BERT Sentence Representations (2021 · 234 citations)
    Pre-trained encoders like BERT do not produce good sentence vectors out of the box when labeled data are unavailable.
  2. Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022 · 113 citations)
    This paper revisits the claim by Min et al. (2022b) that in-context learning (ICL) barely depends on whether demonstration labels are correct. The authors argue that this conclusion came from macro-averaging results over a few datasets and from one particular experimental setup. They show that small but reasonable…

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. Self-Guided Contrastive Learning for BERT Sentence RepresentationsAnnual Meeting of the Association for Computational Linguistics (ACL)Taeuk Kim, Kang Min Yoo, Sang-goo Lee2021 · 234 citations
  2. Ground-Truth Labels Matter: A Deeper Look into Input-Label DemonstrationsConference on Empirical Methods in Natural Language Processing (EMNLP)Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.2022 · 113 citations
  3. Summary Level Training of Sentence Rewriting for Abstractive Summarization ↗Proceedings of the 2nd Workshop on New Frontiers in Summarization · no summary yetSanghwan Bae et al.2019 · 94 citations

 Co-authors here

3 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 178 times (of the 141,840 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/sang-goo-lee/