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Shun Zheng

Shun Zheng has 2 papers in our library, in 2019–2025, most of them on Language models & pretraining, Information extraction, Reasoning; often with Jiang Bian, Junjie Li, Mao Yang.

2 papers in our library · 2019–2025

OpenAlex · ORCID · Wikidata · h-index 16 · 44 works · 1,285 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 124 times in our library · ⭐ Paper Stars ranking: #5653

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

🏢Microsoft Research AsiaBeijing · China2020–2026 · 18 paperscurrent🏢MicrosoftRedmond · United States2021–2024 · 9 papers🎓Tsinghua UniversityBeijing · China2019–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Information extraction · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (2019 · 246 citations)
    Most event extraction (EE) systems only find event arguments within a single sentence. In financial documents, however, the arguments of one event are often spread over several sentences, and one document may describe several events. This work targets document-level EE (DEE) on Chinese financial announcements…
  2. Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs (2025 · 62 citations)
    The paper addresses a disputed question: does reinforcement learning with verifiable rewards (RLVR) make LLMs reason better, or does it only make already-known solutions easier to sample? Earlier work saw base models catch up with RLVR-tuned models on Pass@K at large K.

 Co-authors here

10 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 124 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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