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Jaewoong Cho

Jaewoong Cho has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Dimitris Papailiopoulos, Kangwook Lee, Minki Kang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 6 · 39 works · 100 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 44 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9644

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

🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2016–2026 · 8 paperscurrent🎓University of Wisconsin–MadisonMadison · United States2023–2024 · 2 papers🏛️National Disaster Management Research InstituteUlsan · South Korea2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 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

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks (2024 · 41 citations)
    In-context learning (ICL) has mostly been studied in Transformers. This paper asks whether attention-free state-space models (SSMs), chiefly Mamba and also S4, can acquire the same ability. Large-scale ICL is hard to test cheaply, so the authors follow the Garg et al. (2022) setup: small models are trained from…
  2. Distilling LLM Agent into Small Models with Retrieval and Code Tools (2025 · 11 citations)
    Small language models (sLMs) trained to imitate a large teacher's chain-of-thought (CoT) traces often fail on questions that need rare facts or exact arithmetic.

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. Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning TasksarXiv:2402.04248Jongho Park, Jaeseung Park, Zheyang Xiong, et al.2024 · 41 citations
  2. Distilling LLM Agent into Small Models with Retrieval and Code ToolsarXiv:2505.17612Minki Kang, Jongwon Jeong, Seanie Lee, et al.2025 · 11 citations
  3. Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models ↗arXiv:2411.00686 · no summary yetMinki Kang et al.2024 · 2 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 44 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

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