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Kezhi Kong

Kezhi Kong has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining; often with Bryan Catanzaro, Dan Su, Mohammad Shoeybi.

3 papers in our library · 2023–2025

OpenAlex · h-index 8 · 27 works · 262 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 232 times in our library · ⭐ Paper Stars ranking: #6690

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

🎓University of Maryland, College ParkCollege Park · United States2021–2024 · 7 papers🎓Zhejiang UniversityHangzhou · China2018–2019 · 2 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 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 · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. On the Reliability of Watermarks for Large Language Models (2023 · 103 citations)
    Spotting machine-generated text helps limit spam, bot activity and contamination of future training data. Earlier watermark evaluations, however, mostly assumed the detector receives unmodified model output. This paper tests whether the green-list watermark of Kirchenbauer et al. (2023) survives realistic handling…
  2. Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset (2025 · 65 citations)
    Leading English web corpora built from Common Crawl, such as FineWeb-Edu and DCLM, raise benchmark scores by discarding roughly nine tenths of the crawl with model-based filters.
  3. Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining (2024 · 12 citations)
    Organizations that build large language models rarely disclose how they mix and order pretraining data, so there is little public guidance on how a data blend should be designed and whether it holds up at larger scale.

 Co-authors here

12 of 15 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 232 times (of the 149,439 papers whose bibliographies we read); 70 citing papers with a page here →.

Updated

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