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Yijun Tian

Yijun Tian has 3 papers in our library, in 2024, most of them on Language models & pretraining, Question answering, Information retrieval & search; often with Meng Jiang, Zhaoxuan Tan, Zheyuan Liu.

3 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 14 · 39 works · 505 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 232 times in our library · ⭐ Paper Stars ranking: #3803

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 Notre DameNotre Dame · United States2021–2026 · 26 paperscurrent🎓The University of QueenslandBrisbane · Australia2026 · 2 paperscurrent🎓New York UniversityNew York · United States2019–2023 · 3 papers🎓University of PennsylvaniaPhiladelphia · United States2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 workQuestion answering · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20243 papersYijun Tian's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering (2024 · 234 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. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning (2024 · 83 citations)
    Most current approaches to personalizing large language models work by putting user information into the prompt.
  3. Towards Safer Large Language Models through Machine Unlearning (2024 · 71 citations)
    Large language models can still give dangerous answers to harmful prompts. Earlier unlearning methods based on gradient ascent suppress such answers but tend to damage replies to ordinary requests. The authors propose Selective Knowledge negation Unlearning (SKU), a two-stage framework that removes harmful knowledge…

 Co-authors here

9 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 232 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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