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Zhonghua Li

Zhonghua Li has 2 papers in our library, in 2025, most of them on Language models & pretraining, Information retrieval & search; often with Jiajie Jin, Xiaoxi Li, Ye Qi.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 19 · 95 works · 969 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 27 times in our library · ⭐ Paper Stars ranking: #7984

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

🎓Wuhan Polytechnic UniversityWuhan · China2024–2026 · 6 paperscurrent🔬Nanjing Hydraulic Research InstituteNanjing · China2023–2025 · 8 papers🎓Nanjing UniversityNanjing · China2021–2025 · 7 papers🔬National Laboratory of Solid State MicrostructuresNanjing2021–2025 · 7 papers🎓Fudan UniversityShanghai · China2019–2023 · 5 papers🔬State Key Laboratory of ASIC and SystemShanghai2019–2023 · 5 papers🏢Huawei TechnologiesShenzhen · China2025 · 3 papers🎓North University of ChinaTaiyuan · China2024–2025 · 3 papers🎓Yancheng Institute of TechnologyYancheng · China2024 · 3 papers🔬Collaborative Innovation Center of Advanced MicrostructuresNanjing · China2021–2023 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 32 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 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025 · 25 citations)
    Retrieval-augmented generation (RAG) often feeds an LLM very long retrieved documents. These texts carry much irrelevant material, which raises inference cost and lowers answer quality. Existing compressors either target short chunks or rank tokens with crude signals such as perplexity, so they ignore how a document…
  2. RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025 · 19 citations)
    RetroLLM targets three weaknesses of standard retrieval-augmented generation (RAG): the cost of running a separate retriever, long inputs padded with redundant retrieved chunks, and the fact that retrieval and generation cannot be optimized together.

 Co-authors here

9 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); 10 citing papers with a page here →.

Updated

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