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Zongyu Wu

Zongyu Wu has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Vision & language; often with Fali Wang, Xianren Zhang, Zhiwei Zhang.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 20 works · 152 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 61 times in our library · ⭐ Paper Stars ranking: #6966

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

🎓Pennsylvania State UniversityState College · United States2024–2025 · 10 papers🎓National University of Defense TechnologyChangsha · China2022–2025 · 4 papers

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).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness (2024 · 159 citations)
    LLMs such as Llama-3.1 405B are costly to run and fine-tune. They usually sit behind cloud APIs, which creates privacy risks, and they respond too slowly for on-device or real-time use. They can also fall short in specialised fields like medicine or law. This survey examines small language models (SLMs) as an…
  2. Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models (2026 · 1 citation)
    The paper asks whether someone holding only an image, with no caption, can tell if that image was in the training data of a large vision-language model (LVLM).

 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 61 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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