Elamur Research⌕
☰
home / authors / Shuzhou Yuan

Shuzhou Yuan

Shuzhou Yuan has 2 papers in our library, in 2026, most of them on Language models & pretraining, Interpretability, Bias, fairness & safety; often with Chenming Shang, Ercong Nie, Hengyuan Zhang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 3 · 40 works · 62 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 25 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #9836

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

🎓Technische Universität DresdenDresden · Germany2024–2026 · 20 papersEmploymentcurrent🎓Center for Scalable Data Analytics and Artificial IntelligenceDresden · Germany2024–2026 · 11 paperscurrent🎓Karlsruhe Institute of TechnologyKarlsruhe · Germany2022–2024 · 8 papers🎓University of Maryland, College ParkCollege Park · United States2024 · 2 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 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 3Research subjects and languages the work is about.Language models & pretraining · 2 worksInterpretability · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026 · 9 citations)
    Most earlier reviews of mechanistic interpretability (MI) in LLMs describe what researchers have discovered inside models.
  2. The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models (2026 · 2 citations)
    LLMs increasingly mediate how people find political information, so hidden ideological skews could quietly influence public opinion.

All works

4 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. Evaluating Generative Models for Graph-to-Text Generation ↗Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing · no summary yetShuzhou Yuan, Michael Faerber2023 · 22 citations
  2. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language ModelsFindings of the Association for Computational LinguisticsHengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.2026 · 9 citations
  3. From Monolingual to Bilingual: Investigating Language Conditioning in Large Language Models for Psycholinguistic Tasks ↗Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics · no summary yetShuzhou Yuan et al.2025 · 3 citations
  4. The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language ModelsFindings of the Association for Computational LinguisticsKonrad Löhr, Shuzhou Yuan, Michael Färber2026 · 2 citations

 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 25 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/shuzhou-yuan/