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Wangchunshu Zhou

Wangchunshu Zhou has 3 papers in our library, in 2020–2024, most of them on Language models & pretraining, Efficient models & compression; often with Canwen Xu, Furu Wei, Fuzhao Xue.

3 papers in our library · 2020–2024

OpenAlex · ORCID · h-index 19 · 127 works · 1,505 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 683 citations in our library · 12 more in our library without a page yet · ⭐ Paper Star rank #3404

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

🏢ByteDanceBeijing · China2021–2026 · 8 paperscurrent🎓Zhejiang UniversityHangzhou · China2023–2026 · 5 paperscurrent🎓ETH ZurichZurich · Switzerland2022–2025 · 24 papers🎓Beihang UniversityBeijing · China2019–2023 · 17 papers🎓Stanford UniversityStanford · United States2021–2022 · 6 papers🎓Yale UniversityNew Haven · United States2023–2025 · 5 papers🏢Ant GroupHangzhou · China2024 · 2 papers🎓New York UniversityNew York · United States2023–2024 · 2 papers🎓Shanghai Jiao Tong UniversityShanghai · China2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Arts and Humanities · 3 works Computer Science · 3 works Artificial Intelligence · 3 works Language and Linguistics · 3 works Computer Vision and Pattern Recognition · 2 works

🧭 Topics 2 Efficient models & compression · 1 work Language models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020 · 239 citations)
    Large pretrained Transformers such as BERT are expensive to deploy because they use a lot of memory and run slowly.
  2. RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2023 · 186 citations)
    Open-source LLMs are trained for general use and are weak at imitating specific fictional characters. Strong closed models such as GPT-4 can do this, but they are costly, cannot be fine-tuned, and need all character details placed in the prompt. RoleLLM addresses this with a four-stage pipeline. First, the authors…
  3. OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models (2024 · 61 citations)
    Before this work, few open Mixture-of-Experts (MoE) language models had been trained on trillion-scale, diverse data, and little was known about how their routers actually behave.

Also in our library

12 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

11 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 683 times (of the 141,840 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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