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Chuang Zhou

Chuang Zhou has 5 papers in our library, in 2025–2026, most of them on Information retrieval & search, Language models & pretraining, Reasoning; often with Xiao Xia Huang, Hao Chen, Qinggang Zhang.

5 papers in our library · 2025–2026

OpenAlex · h-index 2 · 11 works · 12 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 71 times in our library · ⭐ Paper Stars ranking: #6999

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

🎓Hong Kong Polytechnic UniversityHong Kong · China2024–2026 · 4 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Computer Science · 5 worksArts and Humanities · 4 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 3 worksInformation Systems · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 3 worksReasoning · 1 work

 Works by year

2020s

20261 paper20254 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models (2025 · 64 citations)
    General-purpose LLMs often fall short in specialized fields. Their domain knowledge is shallow, they struggle with long chains of rule-bound reasoning, and they misread terms whose meaning depends on context. Fine-tuning on domain data can introduce hallucinations and catastrophic forgetting. Classic RAG avoids…
  2. Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025 · 9 citations)
    Text-attributed graphs (TAGs) are graphs whose nodes carry text and whose edges encode relations. Learning on them usually follows a two-stage cascade: a language model produces frozen node embeddings, and a GNN is then trained on those embeddings. The authors argue that this split ignores how text and topology…
  3. You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures (2025 · 9 citations)
    Graph-based RAG systems such as Microsoft GraphRAG, HippoRAG, RAPTOR, LightRAG and KGP first convert the entire corpus into a graph.
  4. Text-Attributed Graph Learning with Coupled Augmentations (2025 · 5 citations)
    Text-attributed graphs are networks whose nodes carry text, such as product descriptions or paper abstracts.
  5. LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026 · 1 citation)
    GraphRAG systems answer questions by reasoning over a knowledge graph built from a text corpus. They tend to resist classic RAG attacks for three reasons: injected documents end up isolated in the graph, embedded instructions are lost when text becomes entity–relation structures, and redundant paths compensate for…

 Co-authors here

12 of 13 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 71 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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