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Jiapu Wang

Jiapu Wang has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Knowledge graphs & ontologies; often with Erik Cambria, Haoran Luo, Rui Mao.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 10 · 22 works · 1,524 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 232 times in our library · ⭐ Paper Stars ranking: #1802

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

🎓Beijing University of TechnologyBeijing · China2022–2026 · 17 paperscurrent🎓Nanjing University of Science and TechnologyNanjing · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksPolitical Science and International Relations · 1 workSocial Sciences · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksKnowledge graphs & ontologies · 1 workEvaluation & benchmarks · 1 workLegal AI · 1 work

💡 Concepts 3Key ideas and methods explained on our concept pages.Knowledge graph · 1 workLarge language models · 1 workArtificial intelligence · 1 work

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023 · 1,551 citations)
    This survey asks how large language models and knowledge graphs can compensate for each other's weaknesses.
  2. Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026 · 3 citations)
    Many people struggle to write good prompts for hard problems, which keeps large language models from performing at their best.
  3. LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026 · 2 citations)
    Current legal benchmarks for LLMs mostly score final answers on technical tasks. They tend to ignore "soft" legal skills such as ethical judgment and assessing social impact, and their static public data may have leaked into training sets. LexGenius is a Chinese expert-level benchmark designed to probe legal general…

 Co-authors here

9 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 232 times (of the 149,439 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated

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