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Zhenyi Wang

Zhenyi Wang has 2 papers in our library, in 2020–2023, most of them on Text generation; often with Bang An, Changyou Chen, Dacheng Tao.

2 papers in our library · 2020–2023

OpenAlex · ORCID · h-index 10 · 37 works · 338 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 98 times in our library · ⭐ Paper Stars ranking: #7486

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

🎓University of Central FloridaOrlando · United States2025–2026 · 6 paperscurrent🎓University of Maryland, College ParkCollege Park · United States2023–2025 · 16 papers🎓University at Buffalo, State University of New YorkBuffalo · United States2020–2023 · 11 papers🎓State University of New YorkAlbany · United States2022–2023 · 2 papers🎓Duke UniversityDurham · United States2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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.Text generation · 1 work

 Works by year

2020s

20231 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020 · 67 citations)
    Neural table-to-text systems often produce descriptions that are unfaithful to their input. They add facts absent from the table (hallucinations) or leave out facts that are present. This happens partly because reference texts in training data are themselves noisy. The authors address this for multi-sentence…
  2. AdaMerging: Adaptive Model Merging for Multi-Task Learning (2023 · 59 citations)
    Merging several fine-tuned copies of one pre-trained model into a single multi-task network is attractive when the original training data are unavailable.

 Co-authors here

9 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 98 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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