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Jiaming Han

Jiaming Han has 2 papers in our library, in 2023, most of them on Language models & pretraining, Vision & language; often with Aojun Zhou, Hongsheng Li, Pan Lu.

2 papers in our library · 2023

OpenAlex · h-index 18 · 39 works · 1,149 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 279 times in our library · ⭐ Paper Stars ranking: #3978

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

🎓Chinese University of Hong KongHong Kong · China2023–2025 · 5 paperscurrent🎓Changchun University of TechnologyChangchun · China2019–2021 · 26 papers🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2023–2024 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention (2023 · 333 citations)
    Turning an open LLM into an instruction follower usually means updating every weight. Stanford Alpaca did this for all 7B parameters of LLaMA, which is slow, expensive, and awkward to repeat for each new use case. The authors propose LLaMA-Adapter, which keeps LLaMA 7B frozen and trains only a small set of additions…
  2. LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model (2023 · 229 citations)
    Making LLaMA follow open-ended instructions about images has usually required updating billions of weights and collecting large multi-modal instruction sets, as MiniGPT-4 and LLaVA do.

 Co-authors here

9 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 279 times (of the 149,439 papers whose bibliographies we read); 80 citing papers with a page here →.

Updated

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