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Qingqing Lyu

Qingqing Lyu has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Hengwei Liu, Weiming Lu, Yin Zhang.

3 papers in our library · 2023–2026

OpenAlex · h-index 1 · 5 works · 7 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 27 times in our library · ⭐ Paper Stars ranking: #8307

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

🎓Zhejiang UniversityHangzhou · China2026 · 2 paperscurrent

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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models (2023 · 34 citations)
    Chinese large language models (LLMs) are improving quickly, but there has been no large Chinese benchmark that tests their knowledge the way the Chinese education system does.
  2. AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026)
    Static, manually built benchmarks are expensive, cover few tasks, and lose discriminative power as LLMs improve.
  3. Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning (2026)
    Small language models struggle to produce hierarchical outlines for Wikipedia-style expository articles. Their outlines tend to skip levels, omit important branches and distribute depth poorly. Existing remedies such as STORM, OmniThink or RAPID rely on costly multi-turn LLM interaction or fixed refinement…

 Co-authors here

12 of 15 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 27 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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