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Xinyue Shen

Xinyue Shen has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Michael Backes, Yang Zhang, Yun Shen.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 14 · 78 works · 821 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 288 times in our library · ⭐ Paper Stars ranking: #5320

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

🔬Helmholtz Center for Information SecuritySaarbrücken · Germany2022–2026 · 39 paperscurrent🔬Poultry Research InstituteYangzhou · China2014–2026 · 9 paperscurrent🏛️Chinese Academy of Agricultural SciencesBeijing · China2014–2025 · 7 papers🎓Yangzhou UniversityYangzhou · China2020–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models (2023 · 329 citations)
    Jailbreak prompts are inputs designed to trick safety-aligned LLMs into producing content their vendors prohibit.
  2. The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026)
    Safety alignment tools cut both ways. The fine-tuning that teaches an LLM to refuse harmful requests can also be used to remove those refusals, and the compromised models can then be shared on open hubs. The authors frame this as an attacker–defender game. They ask which fine-tuning methods most effectively misalign…

 Co-authors here

5 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 288 times (of the 149,439 papers whose bibliographies we read); 98 citing papers with a page here →.

Updated

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