Elamur Research⌕
☰
home / authors / Yuhang Wu

Yuhang Wu

Yuhang Wu has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Bo Huang, Hao Xu, Hao Zhang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 11 · 60 works · 573 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #11888

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

🎓Nanjing University of Aeronautics and AstronauticsNanjing · China2021–2026 · 21 paperscurrent🎓Shanghai University of Engineering ScienceShanghai · China2025–2026 · 2 paperscurrent🎓Nanchang UniversityNanchang · China2017–2024 · 8 papers🎓Guangdong University of TechnologyGuangzhou · China2023–2025 · 5 papers🎓University of California, BerkeleyBerkeley · United States2022–2024 · 4 papers🎓Hohai UniversityNanjing · China2023–2024 · 2 papers🎓Yunnan UniversityKunming · China2020 · 2 papers🎓Shanghai Jiao Tong UniversityShanghai · China2017 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Latent semantic indexing · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026 · 5 citations)
    AdaLoRA improves on LoRA by giving different weight matrices different ranks. It does this by expressing each update in SVD form (P, Λ, Q) and pruning unimportant singular values. The cost is slow convergence and heavy computation during training. HyperAdaLoRA addresses this. Instead of updating P, Λ and Q directly…
  2. Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning (2026)
    Rerankers in RAG pipelines are usually trained on fixed human relevance labels and judged by IR metrics such as NDCG, independent of the LLM that consumes the documents.

 Co-authors here

12 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 5 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yuhang-wu/