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Yuxiao Luo

Yuxiao Luo has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Shikun Zhang, Wei Ye, Yutao Mou.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 14 · 42 works · 428 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 10 times in our library · ⭐ Paper Stars ranking: #9861

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

🎓Sichuan UniversityChengdu · China2022–2026 · 25 paperscurrent🔬Key Laboratory of Leather Chemistry and Engineering of Ministry of EducationChengdu · China2025–2026 · 2 paperscurrent🎓Tongji UniversityShanghai · China2019–2025 · 12 papers🎓Shanghai Jiao Tong UniversityShanghai · China2024–2025 · 3 papers🎓Southwest Jiaotong UniversityChengdu · China2017–2019 · 3 papers🔬State Key Laboratory of Traction PowerChengdu2017–2019 · 3 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 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 · 2 worksSocial Sciences · 2 works

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 2 worksLanguage models & pretraining · 2 worksReasoning · 2 works

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SaRO: Enhancing LLM Safety through Reasoning-based Alignment (2025 · 11 citations)
    Safety-tuned LLMs tend to fail in two opposite ways. Unfamiliar jailbreak prompts can still trick them, yet they also refuse harmless requests that only look risky. The authors find that, in embedding space, jailbreak or harmful prompts often lie close to ordinary ones. They argue that models should reason explicitly…
  2. Thinking Twice Makes Large Language Models Safer and More Helpful (2026)
    Safety alignment of LLMs usually trades usefulness for harmlessness: stronger safety tends to bring more false refusals and lower utility.

 Co-authors here

3 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 10 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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