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Wenyong Huang

Wenyong Huang has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining; often with Can Chen, Dong Li, Fei Mi.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 34 · 228 works · 5,242 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 160 times in our library · ⭐ Paper Stars ranking: #5625

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

🏥Zhongshan Ophthalmic Center, Sun Yat-sen UniversityGuangzhou2001–2026 · 180 paperscurrent🎓Sun Yat-sen UniversityGuangzhou · China2003–2026 · 170 paperscurrent🔬State Key Laboratory of OphthalmologyGuangzhou2006–2026 · 156 paperscurrent🔬Key Laboratory of Guangdong ProvinceGuangzhou · China2022–2024 · 4 papers🎓Northeast Normal UniversityChangchun · China2024–2025 · 3 papers🎓Shanghai Jiao Tong UniversityShanghai · China2025 · 3 papers🏢Huawei TechnologiesShenzhen · China2022–2023 · 3 papers🎓Hong Kong Polytechnic UniversityHong Kong · China2023 · 2 papers🏥The Royal Victorian Eye & Ear HospitalEast Melbourne · Australia2023 · 2 papers🎓Guiyang Medical UniversityGuiyang · China1997–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Aligning Large Language Models with Human: A Survey (2023 · 202 citations)
    Foundation LLMs are pre-trained only to predict the next token. As a result, they often misread user requests, produce biased output, or invent facts. This survey from Huawei Noah's Ark Lab maps research on bringing such models closer to human expectations and organizes it into a three-part taxonomy.
  2. Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs (2025 · 6 citations)
    Pangu Ultra is Huawei's effort to show that a very deep dense Transformer, trained entirely on Ascend NPUs instead of GPUs, can compete with much larger sparse MoE systems.

 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 160 times (of the 149,439 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated

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