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Jianzong Wang

Jianzong Wang has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Ming Li, Ning Cheng, Tianyi Zhou.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 20 · 285 works · 2,380 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 206 times in our library · ⭐ Paper Stars ranking: #4580

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

🏢Ping AnShenzhen2015–2026 · 221 paperscurrent🎓Southern University of Science and TechnologyShenzhen · China2026 · 1 papercurrent🎓Huazhong University of Science and TechnologyWuhan · China2008–2012 · 18 papers🔬Wuhan National Laboratory for OptoelectronicsWuhan · China2008–2012 · 12 papers🎓Guangdong University of TechnologyGuangzhou · China2015 · 3 papers🏢NetEaseBeijing · China2013–2014 · 2 papers🎓Rice UniversityHouston · United States2010 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20241 paperSuperfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning20231 paperFrom Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

 Papers

The most cited first (OpenAlex and our library).

  1. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2023 · 157 citations)
    LIMA showed that instruction tuning depends more on data quality than on volume. Picking good samples out of large open instruction sets still usually takes human curation or an external judge such as ChatGPT. This paper lets the target model choose its own training data. First, a base LLM gets a short warm-up…
  2. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024 · 60 citations)
    Selecting a small, high-quality subset of instruction data can make LLM instruction tuning cheaper and better.

 Co-authors here

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

 Cited by

Papers of our library cite their works 206 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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