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Dongrui Liu

Dongrui Liu has 4 papers in our library, in 2025, most of them on Bias, fairness & safety, Language models & pretraining, Reasoning; often with Jing Shao, Hao Li, Daizong Liu.

4 papers in our library · 2025

OpenAlex · ORCID · h-index 10 · 60 works · 219 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 98 times in our library · ⭐ Paper Stars ranking: #6686

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

🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2024–2026 · 21 paperscurrent🎓Wuhan UniversityWuhan · China2023–2026 · 2 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2018–2025 · 18 papers🔬Shanghai Key Laboratory of Navigation and Location Based ServicesShanghai · China2018–2023 · 5 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 1 workBias, fairness & safety · 3 worksVision & language · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20254 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond (2025 · 55 citations)
    Large Reasoning Models (LRMs) such as DeepSeek-R1, OpenAI o1/o3 and QwQ gain accuracy by writing long chain-of-thought (CoT) traces before answering.
  2. VLSBench: Unveiling Visual Leakage in Multimodal Safety (2025 · 32 citations)
    Earlier work reported a puzzling result: aligning multimodal LLMs with text-only data seemed to make them about as safe as aligning them with image-text pairs.
  3. LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025 · 18 citations)
    The paper investigates a safety weakness of aligned LLMs. Seemingly harmless prompts that are semantically connected to a harmful request can steer a model into producing unsafe output. The authors call this exploitation of "natural distribution shift" and contrast it with attacks that deliberately distort the toxic…
  4. LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint (2025 · 5 citations)
    Combining several fine-tuned LLMs into one through training-free model merging tends to weaken safety. The merged model gains skills such as math or coding but becomes more willing to produce harmful output. The authors trace this to two causes. First, existing methods pick neurons by parameter magnitude, which…

 Co-authors here

12 of 18 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 98 times (of the 149,439 papers whose bibliographies we read); 32 citing papers with a page here →.

Updated

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