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Xiaoxin He

Xiaoxin He has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Question answering, Information retrieval & search; often with Bryan Hooi, Thomas Laurent, Xavier Bresson.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 12 · 42 works · 436 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 190 times in our library · ⭐ Paper Stars ranking: #5460

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

🎓Nanjing Medical UniversityNanjing · China2022–2025 · 6 paperscurrent🏛️Beijing Center for Disease Prevention and ControlBeijing · China2022–2025 · 2 paperscurrent🏥Peking University Shenzhen HospitalShenzhen · China2022–2025 · 2 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2008–2019 · 13 papers🔬Institute of SoftwareBeijing · China2008–2018 · 12 papers🎓National University of SingaporeSingapore · Singapore2021–2024 · 6 papers🏥Nanjing Brain HospitalNanjing · China2022–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workInformation retrieval & search · 1 workQuestion answering · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering (2024 · 236 citations)
    This work targets question answering over real-world graphs whose nodes and edges carry text. Earlier LLM–GNN combinations mostly handled standard classification or toy queries on small synthetic graphs. The authors assemble a GraphQA benchmark from three sources: ExplaGraphs (commonsense reasoning), SceneGraphs…
  2. Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning (2023 · 75 citations)
    Text-attributed graphs (TAGs), such as citation networks where each node carries a title and abstract, are usually handled by GNNs fed with shallow text features (bag-of-words, skip-gram) or by heavy LM+GNN pipelines that need logits or embeddings, which commercial LLMs do not expose.

 Co-authors here

6 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 190 times (of the 149,439 papers whose bibliographies we read); 47 citing papers with a page here →.

Updated

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