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Xiaowen Dong

Xiaowen Dong has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Rui Ye, Siheng Chen, Xianghe Pang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 22 · 104 works · 2,990 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 40 times in our library · ⭐ Paper Stars ranking: #9602

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 OxfordOxford · United Kingdom2018–2026 · 87 papersEmploymentcurrent🎓Massachusetts Institute of TechnologyCambridge · United States2015–2023 · 16 papers🔬MIT Media LabCambridge · United States2015–2023 · 15 papers🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2011–2014 · 8 papers🎓University College LondonLondon · United Kingdom2019 · 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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksHistory and Philosophy of Science · 1 workSafety Research · 1 workSocial Sciences · 1 workArtificial Intelligence · 1 workComputer Science · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review (2024 · 35 citations)
    Peer review is overloaded, and reviewers increasingly use LLMs to draft reviews, but the risks of doing so have not been measured systematically.
  2. Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation (2025 · 19 citations)
    Post-training turns base LLMs into instruction followers, but it needs high-quality instruction data. Such data is expensive to annotate, scarce, and often limited by privacy. Existing synthetic approaches such as Self-Instruct, WizardLM and Magpie usually produce instructions that are not tied to any realistic user…

 Co-authors here

11 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 40 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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