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Mingxu Tao

Mingxu Tao has 2 papers in our library, in 2023–2026, most of them on Legal AI, Information retrieval & search; often with Chen Zhang, Guotong Geng, Wenpeng Hu.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 3 · 16 works · 82 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 118 times in our library · ⭐ Paper Stars ranking: #6105

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

🔬PLA Academy of Military ScienceBeijing · China2026 · 3 paperscurrent🎓Peking UniversityBeijing · China2023–2025 · 16 papers

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 · 2 worksComputer Science · 2 worksSocial Sciences · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksPolitical Science and International Relations · 2 worksLaw · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Legal AI · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Lawyer LLaMA Technical Report (2023 · 157 citations)
    General-purpose LLMs such as Chinese-LLaMA perform poorly on legal questions. They lack specific legal facts, such as the minimum marriage age in China, and even when given the relevant statute they fail to reason from it correctly. The authors propose a three-stage recipe for domain adaptation and use it to build…
  2. When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026)
    Lexical retrieval with BM25 remains surprisingly strong for Chinese legal case retrieval. On LeCaRD-v2, it beats both general and legal-domain dense embedders. The authors therefore improve BM25 instead of replacing it. Their approach rewrites queries with an LLM that follows explicit, human-readable rewriting rules.

 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 118 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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