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Qinghua Zhao

Qinghua Zhao has 2 papers in our library, in 2026, most of them on Reasoning; often with Hao Yang, Lei Li, Xinyu Chen.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 25 · 224 works · 2,303 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #13094

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

🏥The Affiliated Yongchuan Hospital of Chongqing Medical UniversityChongqing · China2008–2026 · 102 paperscurrent🎓Chongqing Medical UniversityChongqing · China2008–2026 · 12 paperscurrent🎓Beihang UniversityBeijing · China2022–2026 · 10 paperscurrent🏥Chinese PLA General HospitalBeijing · China2010–2026 · 7 paperscurrent🎓Hefei UniversityHefei · China2026 · 3 paperscurrent🎓University of CopenhagenCopenhagen · Denmark2023–2026 · 3 paperscurrent🏥Liaocheng People's HospitalLiaocheng · China2009–2023 · 12 papers🎓Wuhan Textile UniversityWuhan · China2024–2025 · 2 papers🎓Taiyuan University of TechnologyTaiyuan · China2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation (2026 · 1 citation)
    Chain-of-Thought (CoT) prompting reliably improves LLM reasoning, but earlier explanations of why it works were mostly intuitive and not tied to model internals.
  2. A Layer-wise Analysis of Supervised Fine-Tuning (2026)
    The paper asks where, across a model's depth, supervised fine-tuning (SFT) actually changes an LLM. Standard LoRA spreads its updates evenly over all layers, which assumes every layer matters equally for alignment. The authors compare base and SFT checkpoints of decoder-only models from 1B to 32B parameters (OLMo2…

 Co-authors here

3 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 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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