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Zheli Liu

Zheli Liu has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Baolei Zhang, Biao Yi, Tong Li.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 36 · 189 works · 4,738 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 11 times in our library · ⭐ Paper Stars ranking: #10646

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

🎓Nankai UniversityTianjin · China2010–2026 · 162 paperscurrent🎓University of North TexasDenton · United States2025–2026 · 3 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2026 · 1 papercurrent🏥First Hospital of China Medical UniversityShenyang · China2010–2025 · 6 papers🎓Jilin UniversityChangchun · China2009–2015 · 5 papers🎓China Medical UniversityShenyang · China2010–2021 · 4 papers🎓Fujian Normal UniversityFuzhou · China2012–2015 · 3 papers🎓Nanjing University of Information Science and TechnologyNanjing · China2017 · 2 papers🎓Guangdong University of TechnologyGuangzhou · China2015 · 2 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksComputer Science · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

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

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Prompt-Guided Internal States for Hallucination Detection of Large Language Models (2025 · 12 citations)
    Supervised hallucination detectors that read an LLM's hidden representations often fail when used outside the domain they were trained on.
  2. CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning (2026 · 3 citations)
    Providers offering fine-tuning as a service face a known risk: a user can upload harmful data and erase a model's safety alignment.
  3. From Personal to Collective: On the Role of Local and Global Knowledge in LLM Personalization (2026)
    Most LLM personalization methods treat every user separately and adapt only to that person's interaction history, using retrieval or per-user adapters such as OPPU.
  4. SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026)
    Membership inference attacks (MIAs) test whether a given text was part of a model's training data, which matters for privacy audits, copyright checks and verifying machine unlearning.

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

Updated

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