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Lei Shen

Lei Shen has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Code & programming languages; often with Chengyu Wang, Jun Huang, Qinkai Zheng.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 7 · 14 works · 463 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 156 times in our library · ⭐ Paper Stars ranking: #3985

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

🎓Wenzhou UniversityWenzhou · China2025 · 1 papercurrent🏛️Ministry of Agriculture and Rural AffairsBeijing · China2021–2022 · 3 papers🎓Northwest A&F UniversityYangling · China2021–2022 · 3 papers🏢Shanghai Fudan MicroelectronicsShanghai · China2014 · 2 papers🎓Beihang UniversityBeijing · China2009–2010 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Code & programming languages · 1 workMultilingual & cross-lingual NLP · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X (2023 · 465 citations)
    Open, functionally checked evaluation of code models across many programming languages was largely missing.
  2. Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards (2026 · 1 citation)
    Small language models usually lag well behind large, expensive LLMs on agentic tool-use tasks. Reinforcement learning could help, but two obstacles hold it back. Public agentic datasets are narrow and often too easy, so RL rollouts give little learning signal. Real APIs are also costly, unstable and limited in scope.…

 Co-authors here

12 of 13 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 156 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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