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Wei Tian

Wei Tian has 2 papers in our library, in 2025–2026, most of them on Reasoning, Evaluation & benchmarks, Legal AI; often with Hao Wang, Jimin Huang, Man Lan.

2 papers in our library · 2025–2026

OpenAlex · h-index 1 · 2 works · 2 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 30 times in our library · ⭐ Paper Stars ranking: #8249

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

🎓East China Normal UniversityShanghai · China2026 · 1 papercurrent🎓Jiangxi Normal UniversityNanchang · China2026 · 1 papercurrent

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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workLaw · 1 workPolitical Science and International Relations · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 2 worksEvaluation & benchmarks · 1 workLegal AI · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LAiW: A Chinese Legal Large Language Models Benchmark (2025 · 35 citations)
    Chinese legal LLMs are usually judged task by task, for example on question answering or consultation. Such scores ignore whether a model actually follows the legal syllogism, the reasoning chain lawyers expect: legal norm (major premise), case facts (minor premise) and judgment (conclusion). This gap makes legal…
  2. CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards (2026 · 1 citation)
    The paper tackles two weaknesses of LLM-based Chinese Grammatical Error Correction (CGEC). General-purpose models know little about typical learner errors. Supervised fine-tuning with MLE also tends to rewrite sentences that need no change, which lowers precision. The authors propose CSRP, a three-stage pipeline…

 Co-authors here

5 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 30 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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