--- title: "Zhijiang Guo — author profile and papers" description: "Zhijiang Guo, Chinese University of Hong Kong: 5 papers on Elamur Research (2019–2024), h-index 20 — affiliations, topics, co-authors and who cites them…" canonical: "https://research.elamur.ai/free/en/authors/zhijiang-guo/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/zhijiang-guo/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Zhijiang Guo # Zhijiang Guo Zhijiang Guo works in natural language processing, on two linked problems: pulling structured facts out of text and judging whether textual claims hold up. Guo's papers here cover [information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/), especially relation extraction with graph neural networks built over dependency trees and document graphs. They also cover automated fact-checking, where systems must retrieve evidence and reach a verdict, and the reliability of large language models when their internal knowledge clashes with what they are given. The most cited work in our library is [A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/) (2022). It sets out a single pipeline running from detecting check-worthy claims, through finding evidence, to predicting a verdict and producing a justification, and it uses that frame to organise existing datasets and methods. Earlier, [Attention Guided Graph Convolutional Networks for Relation Extraction](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/) (2019) replaced hand-written rules for pruning dependency trees with multi-head attention, so the model learns how much each connection should count. [Latent Structure Refinement](https://md.research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/) (2020) carried this graph-based approach to whole documents. It treats the document graph as a latent variable learned during training, which lets relations be inferred across several sentences. The newer papers move toward evidence that comes in mixed forms and toward language models themselves. The FEVEROUS shared task (2021) asked systems to verify claims against both Wikipedia sentences and structured content such as tables and lists. [Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) (2024) sorts disagreements into three kinds: those between a model's stored knowledge and its context, those among the contextual sources, and inconsistencies within the model's own answers. It then traces where each kind comes from. Guo is an Assistant Professor in the DSA Thrust at The [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) (Guangzhou) from 2025. Earlier posts were a doctorate at the [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) (2016–2020), postdoctoral research at the [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) (2020–2022), and a senior researcher role at Huawei's Noah's Ark Lab (2023–2024). Frequent co-authors include [Andreas Vlachos](https://md.research.elamur.ai/free/en/authors/andreas-vlachos/), [Michael Schlichtkrull](https://md.research.elamur.ai/free/en/authors/michael-schlichtkrull/) and [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/), and the work also touches on [reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/). Zhijiang Guo has 5 papers in our library, in 2019–2024, most of them on [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Andreas Vlachos](https://md.research.elamur.ai/free/en/authors/andreas-vlachos/), [Michael Schlichtkrull](https://md.research.elamur.ai/free/en/authors/michael-schlichtkrull/), [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/). 5 papers in our library · 2019–2024 [OpenAlex](https://openalex.org/A5101206779) · [ORCID](https://orcid.org/0000-0002-6232-5957) · [Wikidata](https://www.wikidata.org/wiki/Q131008882) · [**h-index 20**](https://openalex.org/authors/A5101206779) · [84 works](https://openalex.org/works?filter=author.id:A5101206779) · [2,367 citations](https://openalex.org/works?filter=author.id:A5101206779&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 5 works in our library](https://md.research.elamur.ai/free/en/authors/zhijiang-guo/#papers) · [🔗 1,146 citations in our library](https://md.research.elamur.ai/free/en/authors/zhijiang-guo/cited-by/) · [15 more in our library without a page yet](#also) · [⭐ Paper Star rank #1196](https://md.research.elamur.ai/free/en/paper-stars/page/12/#star-1196) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Zhijiang+Guo) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Zhijiang+Guo) · [dblp](https://dblp.org/search?q=Zhijiang+Guo) · [ACL Anthology](https://aclanthology.org/search/?q=Zhijiang+Guo) ## Affiliations [🎓**The Hong Kong University of Science and Technology (Guangzhou)** Guangzhou · China 2023–2026 · 15 papersEmployment since 2025 · Assistant Professor, DSA Thrust*current*](https://md.research.elamur.ai/free/en/universities/the-hong-kong-university-of-science-and-technology-guangzhou/) · [🎓**Hong Kong University of Science and Technology** Hong Kong · Hong Kong 2023–2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) · [🎓**Chinese University of Hong Kong** Hong Kong · Hong Kong 2025–2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) · [🎓**University of Cambridge** Cambridge · United Kingdom 2020–2025 · 26 papersEmployment 2020–2022 · Postdoctoral Researcher, Computer Science and Technology](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) · [🏢**Huawei Technologies** Shenzhen · China 2023–2025 · 9 papersEmployment 2023–2024 · Senior Researcher, Noah's Ark Lab](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🎓**China Academy of Chinese Medical Sciences** Beijing · China 2024–2025 · 6 papers](https://ror.org/042pgcv68) · [🏥**Guang’anmen Hospital** Beijing · China 2024–2025 · 6 papers](https://ror.org/04gjmb875) · [🎓**Singapore University of Technology and Design** Singapore · Singapore 2016–2020 · 6 papersEducation 2016–2020 · Doctor, ISTD](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) · [🎓**City University of Hong Kong** Hong Kong · Hong Kong 2024–2025 · 2 papers](https://md.research.elamur.ai/free/en/universities/city-university-of-hong-kong/) · [🎓**King's College London** London · United Kingdom 2021 · 2 papers](https://md.research.elamur.ai/free/en/universities/king-s-college-london/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed. ## Topics [Information extraction (2)](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning (1)](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2024** 1 paper](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/aly-2021-fact-extraction-verification-over-unstructured/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/) ### 2010s [**2019** 1 paper](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/) ## Papers The most cited first (OpenAlex and our library). 1. **[A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)** *(2022 · 646 citations)* Manual fact-checking cannot keep pace with how quickly claims, true or false, spread online. Verifying a single statement can take professionals hours or even days. This TACL survey from the University of Cambridge maps NLP research on automating that work. Earlier overviews either focused on isolated subtasks or… 2. **[Attention Guided Graph Convolutional Networks for Relation Extraction](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/)** *(2019 · 597 citations)* The paper addresses relation extraction, which means identifying how entities in text are related. Dependency trees help with this task, but earlier systems relied on fixed pruning rules, such as keeping only the shortest dependency path, the LCA subtree, or tokens within distance K of that path. These rules can… 3. **[Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://md.research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/)** *(2020 · 379 citations)* This ACL 2020 paper addresses document-level relation extraction, where a relation between two entities often can only be inferred by chaining evidence across several sentences. 4. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)** *(2024 · 179 citations)* This survey examines a recurring problem in large language models: the knowledge stored in a model's weights can disagree with information supplied at inference time, and supplied sources can disagree with each other. 5. **[The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task](https://md.research.elamur.ai/free/en/papers/aly-2021-fact-extraction-verification-over-unstructured/)** *(2021 · 178 citations)* The FEVEROUS shared task, held at the FEVER 2021 workshop, extends automated fact checking from purely textual evidence to a mix of text and structured Wikipedia content such as tables and lists. ## Also in our library 15 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 12 co-authors with a page, the most papers together first. - [Andreas Vlachos](https://md.research.elamur.ai/free/en/authors/andreas-vlachos/) ×2 - [Michael Schlichtkrull](https://md.research.elamur.ai/free/en/authors/michael-schlichtkrull/) ×2 - [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/) ×2 - [Arpit Mittal](https://md.research.elamur.ai/free/en/authors/arpit-mittal/) - [Christos Christodoulopoulos](https://md.research.elamur.ai/free/en/authors/christos-christodoulopoulos/) - [Cunxiang Wang](https://md.research.elamur.ai/free/en/authors/cunxiang-wang/) - [James Thorne](https://md.research.elamur.ai/free/en/authors/james-thorne/) - [Oana Cocarascu](https://md.research.elamur.ai/free/en/authors/oana-cocarascu/) - [Wei Xu](https://md.research.elamur.ai/free/en/authors/wei-xu/) - [Yan Zhang](https://md.research.elamur.ai/free/en/authors/yan-zhang/) - [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) - [Zehan Qi](https://md.research.elamur.ai/free/en/authors/zehan-qi/) ## Cited by Papers of our library cite their works 1,146 times (of the 140,884 papers whose bibliographies we read); [34 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/zhijiang-guo/cited-by/).