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Zhengfu He

Zhengfu He has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Xipeng Qiu, Xuanjing Huang, Lingjie Chen.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 8 · 14 works · 428 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 109 times in our library · ⭐ Paper Stars ranking: #6482

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

🎓Fudan UniversityShanghai · China2022–2024 · 8 paperscurrent🏥Sir Run Run Shaw HospitalHangzhou · China2020–2024 · 6 paperscurrent🎓Zhejiang UniversityHangzhou · China2020–2024 · 6 paperscurrent🏥Second Affiliated Hospital of Zhejiang UniversityHangzhou · China2024 · 2 paperscurrent🎓Shanghai Innovation InstituteShanghai · China2024 · 1 papercurrent

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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models (2023 · 136 citations)
    Text is discrete, which makes diffusion models hard to apply to it. Earlier diffusion approaches for text also ignored pre-trained language models. DiffusionBERT addresses this by noting that BERT and diffusion models both learn to denoise. The authors use BERT as the network that reverses a discrete diffusion…
  2. Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders (2024 · 63 citations)
    Sparse Autoencoders (SAEs) are a leading unsupervised tool for splitting language-model activations into sparse, interpretable features.

 Co-authors here

8 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 109 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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