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Mengzhao Chen

Mengzhao Chen has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Efficient models & compression; often with Kaipeng Zhang, Peng Gao, Peng Xu.

2 papers in our library · 2023–2025

OpenAlex · h-index 11 · 24 works · 356 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 225 times in our library · ⭐ Paper Stars ranking: #4312

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

🎓Zhejiang UniversityHangzhou · China2023–2026 · 9 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2023–2025 · 4 papers🎓University of Hong KongPok Fu Lam · China2024–2025 · 4 papers🎓Xiamen UniversityXiamen · China2022–2023 · 4 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20251 paperMengzhao Chen's papers of 202520231 paperMengzhao Chen's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023 · 193 citations)
    Compressing large language models to very low bit-widths usually forces a trade-off. Post-training quantization (PTQ) is cheap, but it relies on hand-tuned parameters and degrades badly at settings such as W2A16 or W4A4. Quantization-aware training (QAT) keeps accuracy but needs heavy compute and data. OmniQuant aims…
  2. EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025 · 82 citations)
    Quantization-aware training (QAT) can compress large language models to very low bit-widths with little accuracy loss.

 Co-authors here

6 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 225 times (of the 141,840 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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