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Jinfa Huang

Jinfa Huang has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning, Vision & language; often with Chengfeng Mao, Dawei Zhu, Fenglin Liu.

3 papers in our library · 2023–2025

OpenAlex · h-index 9 · 23 works · 298 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 79 times in our library · ⭐ Paper Stars ranking: #6501

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 RochesterRochester · United States2024–2026 · 9 paperscurrent🎓Jiangxi University of Science and TechnologyGanzhou · China2023–2026 · 2 paperscurrent🎓Peking UniversityBeijing · China2020–2025 · 11 papers🎓University of Electronic Science and Technology of ChinaChengdu · China2024 · 2 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksMedicine · 1 workArts and Humanities · 2 worksHealth Informatics · 1 workLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20252 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Large Language Models in Medicine: Progress, Application, and Challenge (2023 · 87 citations)
    This survey maps how large language models are being adapted to medicine. It covers how they are built, how well they perform, and what blocks their clinical use. Earlier reviews mostly described ChatGPT-style applications through small case studies. They offered little guidance on constructing medical models or…
  2. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025 · 22 citations)
    Large vision–language models (LVLMs) can learn tasks from interleaved image–text demonstrations, but their in-context learning (ICL) is fragile. Small changes to the chosen demonstrations (ICDs) can noticeably shift outputs, and existing selection methods rely on hand-crafted relevance metrics.
  3. A Survey on Latent Reasoning (2025 · 15 citations)
    Chain-of-thought prompting makes LLMs reason step by step in written language, but each generated token carries very little information.

 Co-authors here

12 of 14 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 79 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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