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Qianyu He

Qianyu He has 2 papers in our library, in 2023–2026, most of them on Evaluation & benchmarks; often with Fei Yu, Jiaqing Liang, Jie Zeng.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 15 · 57 works · 1,012 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 89 times in our library · ⭐ Paper Stars ranking: #8209

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

🎓Fudan UniversityShanghai · China2023–2025 · 15 papers🎓Heilongjiang Bayi Agricultural UniversityDaqing · China2014–2025 · 10 papers🏛️Chinese Academy of SciencesBeijing · China2009–2022 · 8 papers🎓Center for Excellence in Molecular Plant SciencesShanghai · China2009–2015 · 7 papers🔬Shanghai Institutes for Biological SciencesShanghai · China2009–2015 · 7 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation (2023 · 34 citations)
    Benchmarks such as MMLU, BIG-bench and HELM quickly become too easy for modern LLMs and may leak into their training data.
  2. Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following (2026 · 3 citations)
    Language models often fail to satisfy instructions that contain several constraints at once. Existing RL methods for this problem depend on stronger models or human annotators for rewards, and multi-constraint tasks give sparse rewards. The authors propose a self-supervised RL framework in which reward signals come…

 Co-authors here

11 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 89 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated

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