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Jongwoo Ko

Jongwoo Ko has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Efficient models & compression, Reasoning; often with Se-Young Yun, Sungnyun Kim, Tianyi Chen.

3 papers in our library · 2024–2026

OpenAlex · h-index 6 · 37 works · 166 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 168 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6234

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

🏢MicrosoftRedmond · United States2024–2026 · 5 paperscurrent🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2021–2024 · 15 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 2 worksLanguage models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DistiLLM: Towards Streamlined Distillation for Large Language Models (2024 · 112 citations)
    DistiLLM tackles knowledge distillation (KD) for auto-regressive language models, where a large teacher LLM is compressed into a smaller student.
  2. DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs (2025 · 32 citations)
    Knowledge distillation (KD) for LLMs usually applies the same divergence to teacher-generated outputs (TGOs) and student-generated outputs (SGOs).
  3. From Belief Entrenchment to Robust Reasoning in LLM Agents (2026 · 2 citations)
    Multi-Agent Debate (MAD), where several LLM instances critique one another at inference time, is often expected to repair reasoning errors.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. DistiLLM: Towards Streamlined Distillation for Large Language ModelsarXiv:2402.03898Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.2024 · 112 citations
  2. DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsarXiv:2503.07067Jongwoo Ko, Tianyi Chen, Sungnyun Kim, et al.2025 · 32 citations
  3. Scaling Reasoning Efficiently via Relaxed On-Policy Distillation ↗arXiv:2603.11137 · no summary yetJongwoo Ko et al.2026 · 18 citations
  4. Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judge ↗arXiv (Cornell University) · no summary yetAparna Elangovan et al.2024 · 9 citations
  5. From Belief Entrenchment to Robust Reasoning in LLM AgentsTransactions of the Association for Computational Linguistics (TACL)Jihwan Oh, Minchan Jeong, Jongwoo Ko, et al.2026 · 2 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 168 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jongwoo-ko/