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Yuheng Jia

Yuheng Jia has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Ao Sun, Junfeng Fang, Tat-Seng Chua.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 26 · 107 works · 2,116 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 14 times in our library · ⭐ Paper Stars ranking: #8965

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

🎓Southeast UniversityNanjing · China2020–2026 · 66 paperscurrent🎓South China University of TechnologyGuangzhou · China2026 · 1 papercurrent🎓City University of Hong KongHong Kong · China2016–2021 · 26 papers🎓Saint Francis UniversityHong Kong · China2023–2025 · 4 papers🎓Southeast UniversityDhaka · Bangladesh2024–2025 · 3 papers🎓Nanning Normal UniversityNanning · China2022–2024 · 2 papers🎓Nanjing UniversityNanjing · China2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 7The 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 workSafety Research · 1 workSocial Sciences · 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. Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence (2025 · 21 citations)
    Large vision-language models (LVLMs) often hallucinate, describing objects that are not in the image. Most existing remedies retrain the model or adjust output logits after generation. This paper instead looks inside the multi-head attention module. The authors define Vision-aware Head Divergence (VHD), which…
  2. CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026 · 1 citation)
    Standard LLM alignment fits one shared set of weights to pooled human feedback. That works when a single correct answer exists, but it breaks down on culturally contested questions. The authors argue that the value distributions of different demographic groups form well-separated clusters, which they call Cultural…

 Co-authors here

4 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 14 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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