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Zhengyuan Shen

Zhengyuan Shen has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Shuai Wang, Boran Han, Han Ding.

2 papers in our library · 2025–2026

OpenAlex · h-index 13 · 51 works · 531 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 19 times in our library · ⭐ Paper Stars ranking: #9323

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

🏢AmazonSeattle · United States2023–2026 · 8 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2026 · 2 paperscurrent🎓University of North Carolina at Chapel HillChapel Hill · United States2026 · 1 papercurrent🎓Henan UniversityKaifeng · China2022–2025 · 10 papers🎓University of MinnesotaMinneapolis · United States2019–2025 · 10 papers🎓University of Minnesota SystemMinneapolis · United States2019–2025 · 7 papers🏛️Ministry of Education of the People's Republic of ChinaBeijing · China2023–2025 · 3 papers🎓Zhejiang University of TechnologyHangzhou · China2019–2022 · 3 papers🎓Beijing Institute of TechnologyBeijing · China2025 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Systematic Survey of Automatic Prompt Optimization Techniques (2025 · 40 citations)
    Large language models react unpredictably to small changes in prompt wording, ordering or formatting. Manual prompt engineering is therefore laborious and quickly becomes outdated as models change. This survey by an Amazon Web Services team reviews black-box Automatic Prompt Optimization (APO): methods that improve…
  2. When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors (2026)
    Even when LLMs parse a table correctly, they often misread it during reasoning. They cite the wrong cell, mix up columns, or skip relevant rows. The authors call these slips data referencing errors (DREs) and split them into two types: Incorrect Citation and Omitted Information. Earlier work looked at DREs only…

 Co-authors here

11 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 19 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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