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Dan Shi

Dan Shi has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety, Evaluation & benchmarks; often with Deyi Xiong, Renren Jin, Zhuowen Han.

3 papers in our library · 2023–2026

OpenAlex · h-index 9 · 18 works · 223 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 118 times in our library · ⭐ Paper Stars ranking: #5845

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

🎓Tianjin UniversityTianjin · China2023–2026 · 3 paperscurrent🎓Ocean University of ChinaQingdao · China2010–2013 · 7 papers🎓Northwest A&F UniversityYangling · China2024 · 3 papers🎓Shanghai University of Traditional Chinese MedicineShanghai · China2024 · 2 papers🏥Yueyang HospitalShanghai · China2024 · 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksBias, fairness & safety · 2 worksEvaluation & benchmarks · 1 workInterpretability · 1 work

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating Large Language Models: A Comprehensive Survey (2023 · 182 citations)
    This survey from Tianjin University (Guo, Jin, Liu et al., 2023) maps the fast-growing field of evaluating large language models.
  2. Neuronal Insights into LLM Attacks: Targeted Neuron Tuning for Precise and Robust Vulnerability Patching (2026)
    Safety-aligned LLMs can still be jailbroken, and it is not well understood which internal components such attacks exploit.
  3. Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models (2026)
    Models post-trained with reinforcement learning (RL) often improve on tasks outside their training domain, while supervised fine-tuning (SFT) tends to erode general abilities.

 Co-authors here

12 of 13 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 118 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

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