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Zhuoshu Li

Zhuoshu Li has 2 papers in our library, in 2024, most of them on Language models & pretraining, Vision & language; often with Bingxuan Wang, Bo Liu, Bo Zhang.

2 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 6 · 11 works · 99 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 492 times in our library · ⭐ Paper Stars ranking: #5472

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

🎓Zhejiang UniversityHangzhou · China2021–2025 · 9 papersEducationcurrent🏢DeepSeekHangzhou · ChinaEmployment

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).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 · 2 worksVision & language · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DeepSeek-VL: Towards Real-World Vision-Language Understanding (2024 · 208 citations)
    DeepSeek-VL is an open-source vision-language model from DeepSeek-AI. It targets a known problem: open models score well on academic benchmarks but often fall short of proprietary systems like GPT-4V in practical use. The authors attribute this to three causes: too little multimodal pretraining, instruction data…
  2. Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024 · 17 citations)
    Most parameter-efficient fine-tuning research targets dense LLMs, so sparse Mixture-of-Experts (MoE) models have had little dedicated support.

 Co-authors here

12 of 13 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 492 times (of the 149,439 papers whose bibliographies we read); 128 citing papers with a page here →.

Updated

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