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Seanie Lee

Seanie Lee has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Minki Kang, Sung Ju Hwang, Jinheon Baek.

2 papers in our library · 2023–2025

OpenAlex · h-index 7 · 21 works · 227 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 92 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7115

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 Korea2020–2026 · 16 paperscurrent🎓University of CambridgeCambridge · United Kingdom2024 · 2 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks (2023 · 71 citations)
    Small language models are cheaper to run than API-only LLMs and better for privacy. However, they perform poorly when answers depend on large amounts of factual or domain knowledge, because their few parameters cannot store it all. Standard reasoning distillation trains a small model to reproduce rationales written…
  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. Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetDong Bok Lee et al.2020 · 97 citations
  2. Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive TasksConference on Neural Information Processing Systems (NeurIPS)Minki Kang, Seanie Lee, Jinheon Baek, et al.2023 · 71 citations
  3. Distilling LLM Agent into Small Models with Retrieval and Code ToolsarXiv:2505.17612Minki Kang, Jongwon Jeong, Seanie Lee, et al.2025 · 11 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 92 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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