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Seongyun Lee

Seongyun Lee has 2 papers in our library, in 2025, most of them on Language models & pretraining, Evaluation & benchmarks, Code & programming languages; often with Carolin Lawrence, Graham Neubig, Jinheon Baek.

2 papers in our library · 2025

OpenAlex · h-index 5 · 15 works · 72 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 107 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #9541

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 Korea2023–2025 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksDecision Sciences · 1 workInformation Systems · 1 workInformation Systems and Management · 1 work

🧭 Topics 3Research subjects and languages the work is about.Code & programming languages · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating Language Models as Synthetic Data Generators (2025 · 32 citations)
    Synthetic data now drives much of language model post-training. Earlier data-generation studies, however, each used different generator LMs, data volumes, student models and benchmarks, so their results cannot be compared to show which LM produces the most useful training data. Kim et al. address this with…
  2. Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning (2025 · 24 citations)
    Many machine learning papers are published without code. Across ICLR, ICML and NeurIPS 2024, only about one in five accepted papers released an official implementation, so reproducing results often requires slow reverse-engineering. The authors ask whether an LLM system can build a working repository from the paper…

All works

5 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. The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetSeungone Kim et al.2025 · 57 citations
  2. Evaluating Language Models as Synthetic Data GeneratorsAnnual Meeting of the Association for Computational Linguistics (ACL)Seungone Kim, Juyoung Suk, Xiang Yue, et al.2025 · 32 citations
  3. Paper2Code: Automating Code Generation from Scientific Papers in Machine LearningarXiv:2504.17192Minju Seo, Jinheon Baek, Seongyun Lee, et al.2025 · 24 citations
  4. The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think ↗arXiv (Cornell University) · no summary yetSeongyun Lee et al.2025 · 4 citations
  5. Scaling Evaluation-Time Compute with Reasoning Models as Evaluators ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetSeungone Kim et al.2026

 Co-authors here

11 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 107 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

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