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Zhenghao Lin

Zhenghao Lin has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Chen Lin, Jian Jiao, Nan Duan.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 13 · 34 works · 467 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 176 times in our library · ⭐ Paper Stars ranking: #6346

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

🎓South China University of TechnologyGuangzhou · China2019–2026 · 10 paperscurrent🤝Shanghai Institute of Measurement and Testing TechnologyShanghai · China2022–2025 · 6 papers🎓Xiamen UniversityXiamen · China2022–2024 · 3 papers🎓Tsinghua UniversityBeijing · China2015–2019 · 3 papers🏥Shenzhen Third People’s HospitalShenzhen · China2021–2025 · 2 papers🎓Southern University of Science and TechnologyShenzhen · China2021–2025 · 2 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 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators (2023 · 152 citations)
    Manually labelling data for NLP is slow and expensive. This paper asks whether GPT-3.5 models can replace crowd workers. The authors build AnnoLLM, which gives the model the same materials a human annotator would get: a task description, label definitions and labelled examples. It then adds a two-stage…
  2. Rho-1: Not All Tokens Are What You Need (2024 · 59 citations)
    Standard LLM pre-training applies the next-token loss uniformly, even though filtered corpora still contain noisy, irrelevant, or already-mastered tokens.

 Co-authors here

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

 Cited by

Papers of our library cite their works 176 times (of the 149,439 papers whose bibliographies we read); 42 citing papers with a page here →.

Updated

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