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Yulong Wu

Yulong Wu has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Semantic parsing & meaning representation; often with Guozheng Rao, Hanqing Wu, Jiahuan Zhang.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 11 works · 28 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 11 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #11089

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

🎓University of ManchesterManchester · United Kingdom2019–2026 · 4 papersEducation until 2026Employment until 2026Education 2019–2020 · MSc Advanced Computer Science with Specialisation in Artificial Intelligence, Department of Computer ScienceEducation since 2021 · PhD Computer Science, Department of Computer ScienceEmployment 2020–2021 · Research Assistant, NIHR Greater Manchester Patient Safety Translational Research CentreEmployment 2021 · Research Assistant, School of Social Sciencescurrent🎓Heilongjiang UniversityHarbin · China2015–2019Education 2015–2019 · BEng The Internet of Things Engineering, Department of Computer Science

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksSemantic parsing & meaning representation · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SR-LLM: Rethinking the Structured Representation in Large Language Model (2025 · 11 citations)
    Structured representations such as AMR, parse syntax trees (PST) and first-order logic (FOL) have long been central to NLP.
  2. Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World (2026)
    Most QA robustness tests use artificial perturbations from fixed manipulation rules. These miss the meaning-preserving changes that real documents undergo as people edit them. This matters for LLMs in RAG and agent systems, which read sources that keep changing. The authors build an automatic pipeline that mines…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. SR-LLM: Rethinking the Structured Representation in Large Language ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Jiahuan Zhang, Tianheng Wang, Hanqing Wu, et al.2025 · 11 citations
  2. Are Machine Reading Comprehension Systems Robust to Context Paraphrasing? ↗Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetYulong Wu, Viktor Schlegel, Riza Batista-Navarro2023 · 2 citations
  3. MMT's Submission for the WMT 2023 Quality Estimation Shared Task ↗Proceedings of the Eighth Conference on Machine Translation · no summary yetYulong Wu et al.2023 · 2 citations
  4. Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real WorldFindings of the Association for Computational LinguisticsYulong Wu, Viktor Schlegel, Riza Batista-Navarro2026

 Co-authors here

10 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 11 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

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