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Xiaojun Xu

Xiaojun Xu has 2 papers in our library, in 2017–2023, most of them on Language models & pretraining, Bias, fairness & safety; often with Chang Liu, Dawn Song, Yang Liu.

2 papers in our library · 2017–2023

OpenAlex · ORCID · Wikidata · h-index 30 · 82 works · 2,861 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 218 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4048

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

🎓Zhejiang A & F UniversityLin’an Shi · China2009–2026 · 57 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2020–2023 · 6 papers🎓Beijing Forestry UniversityBeijing · China2013 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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 worksLanguage and Linguistics · 2 worksComputer Networks and Communications · 1 work

🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning (2017 · 372 citations)
    The paper addresses NL2SQL: turning a natural-language question plus a table schema into an SQL query. Standard sequence-to-sequence decoders must commit to one linear ordering of the output. Yet WHERE conditions can be listed in any order without changing the result, which makes training sensitive to an arbitrary…
  2. Large Language Model Unlearning (2023 · 174 citations)
    The paper treats unlearning as a cheap alignment tool for LLMs. The idea is to make a model drop unwanted behaviour using only negative examples, such as harmful replies found through red teaming or user reports, without the human-written positive answers that RLHF needs. The authors define the setting, goals and…

All works

3 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. SQLNet: Generating Structured Queries From Natural Language Without Reinforcement LearningarXiv:1711.04436Xiaojun Xu, Chang Liu, Dawn Song2017 · 372 citations
  2. Large Language Model UnlearningConference on Neural Information Processing Systems (NeurIPS)Yuanshun Yao, Xiaojun Xu, Yang Liu2023 · 174 citations
  3. Effective and Efficient Federated Tree Learning on Hybrid Data ↗arXiv (Cornell University) · no summary yetQinbin Li et al.2023 · 2 citations

 Co-authors here

4 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 218 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated

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