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Wei Huang

Wei Huang has 2 papers in our library, in 2023, most of them on Language models & pretraining, Bias, fairness & safety; often with Changshun Wu, Chunyu Kit, Imran Razzak.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 8 · 45 works · 365 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 63 times in our library · ⭐ Paper Stars ranking: #5941

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

🎓Central South UniversityChangsha · China2018–2025 · 9 paperscurrent🎓University of Hong KongPok Fu Lam · China2024–2025 · 4 paperscurrent🏢Chunghwa TelecomTaipei · Taiwan2018–2025 · 2 paperscurrent🎓Changchun University of Science and TechnologyChangchun · China2025 · 1 papercurrent🔬Purple Mountain LaboratoriesNanjing · China2025 · 1 papercurrent🎓Communication University of ChinaBeijing · China2015–2021 · 6 papers🎓Guangxi Technological College of Machinery and ElectricityNanning · China2013 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 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 10The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksEngineering · 1 workArtificial Intelligence · 2 worksSafety, Risk, Reliability and Quality · 1 workSoftware · 1 workArts and Humanities · 1 workMaterials Science · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 workMaterials Chemistry · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation (2023 · 199 citations)
    The survey examines how to make large language models safe and trustworthy as they move quickly into industrial and consumer use.
  2. DARWIN Series: Domain Specific Large Language Models for Natural Science (2023 · 55 citations)
    General-purpose instruction-tuned LLMs perform poorly on natural science. Fine-tuned GPT models are closed and must be adapted on OpenAI's paid servers. Open alternatives are usually tuned on instructions written by GPT-3.5/GPT-4, which can reduce factual accuracy. The authors address this with DARWIN, a family of…

 Co-authors here

7 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 63 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

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