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Lei Shi

Lei Shi has 3 papers in our library, in 2004–2026, most of them on Semantic role labeling, Language models & pretraining, Semantic parsing & meaning representation; often with Chao Huang, Dawei Yin, Jiabin Tang.

3 papers in our library · 2004–2026

OpenAlex · ORCID · h-index 6 · 12 works · 467 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 94 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5563

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

🎓Newcastle UniversityNewcastle upon Tyne · United Kingdom2026 · 1 papercurrent🏢BaiduBeijing · China2024–2025 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workSemantic parsing & meaning representation · 1 workSemantic role labeling · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20261 paper20231 paper

2000s

20041 paper

 Papers

The most cited first (OpenAlex and our library).

  1. GraphGPT: Graph Instruction Tuning for Large Language Models (2023 · 268 citations)
    Graph neural networks usually still need labels from the target task, even when pre-trained with self-supervised objectives such as contrastive (DGI, GCA) or generative (GraphMAE, S2GAE) learning.
  2. Open Text Semantic Parsing Using FrameNet and WordNet (2004 · 30 citations)
    The paper tackles a core weakness of syntax-based language understanding: one syntactic pattern can carry different meanings, and one meaning can surface in many syntactic forms.
  3. GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR (2026)
    Parameter-efficient fine-tuning methods are mostly designed for SFT, but RLVR changes model weights in a different way.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. GraphGPT: Graph Instruction Tuning for Large Language ModelsInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)Jiabin Tang, Yuhao Yang, Wei Wei, et al.2023 · 268 citations
  2. UrbanGPT: Spatio-Temporal Large Language Models ↗ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · no summary yetZhonghang Li et al.2024 · 189 citations
  3. Open Text Semantic Parsing Using FrameNet and WordNetConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Lei Shi, Rada Mihalcea2004 · 30 citations
  4. GeoRA: Geometry-Aware Low-Rank Adaptation for RLVRAnnual Meeting of the Association for Computational Linguistics (ACL)Jiaying Zhang, Lei Shi, Jiguo Li, et al.2026

 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 94 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

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