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Zhiwei Fei

Zhiwei Fei has 3 papers in our library, in 2023–2026, most of them on Legal AI, Language models & pretraining; often with Kai Chen, Dawei Zhu, Fengzhe Zhou.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 1 · 10 works · 3 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 174 times in our library · ⭐ Paper Stars ranking: #5724

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

🎓Nanjing UniversityNanjing · China2026 · 6 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksSocial Sciences · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksLaw · 2 worksPolitical Science and International Relations · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Legal AI · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LawBench: Benchmarking Legal Knowledge of Large Language Models (2023 · 186 citations)
    Existing legal benchmarks for LLMs mostly target English-language common-law systems (US, EU) or bar-exam-style questions.
  2. InternLM-Law: An Open Source Chinese Legal Large Language Model (2024 · 9 citations)
    General-purpose LLMs often fail on Chinese legal questions, and earlier Chinese legal models were narrow, consultation-oriented, and built on older backbones.
  3. OpenCompass: A Universal Evaluation Platform for Large Language Models (2026 · 1 citation)
    Judging an LLM's overall quality usually means running dozens or hundreds of static benchmarks. Each benchmark has its own scoring method (regex matching, ROUGE, model-based judging), its own sensitivity to prompts, and its own data preparation and output cleanup. This fragmentation makes large, cross-domain…

 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 174 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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