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Zhouxing Shi

Zhouxing Shi has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Bias, fairness & safety, Vision & language; often with Cho-Jui Hsieh, Fan Yin, G M Shahariar.

3 papers in our library · 2024–2026

OpenAlex · h-index 12 · 42 works · 510 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 41 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7474

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

🎓University of California, RiversideRiverside · United States2024–2026 · 9 paperscurrent🎓University of California, Los AngelesLos Angeles · United States2021–2025 · 11 papers🎓Tsinghua UniversityBeijing · China2019–2020 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 2 worksLanguage models & pretraining · 2 worksVision & language · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Red Teaming Language Model Detectors with Language Models (2024 · 62 citations)
    Detectors that flag LLM-generated text are proposed as a safeguard against misuse such as plagiarism or mass misinformation, but it was unclear how well they resist deliberate evasion.
  2. From Individual to Common: An Early Exploration of Consensus in Non-verifiable Data for Balanced Preference Optimization (2026 · 1 citation)
    Preference tuning of LLMs usually relies on response pairs with an obvious quality difference, scored by one strong judge.
  3. PII-VisBench: Evaluating Personally Identifiable Information Safety in Vision Language Models Along a Continuum of Visibility (2026)
    Most privacy tests for vision language models check whether a model hands over personal data on request. They ignore how much is already known about the pictured person online. PII-VisBench fills this gap. Each image is sorted by how findable its subject is on the web, and models are asked for personal details based…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Red Teaming Language Model Detectors with Language ModelsTransactions of the Association for Computational Linguistics (TACL)Zhouxing Shi, Yihan Wang, Fan Yin, et al.2024 · 62 citations
  2. Robustness to Modification with Shared Words in Paraphrase Identification ↗Findings of the Association for Computational Linguistics: EMNLP 2020 · no summary yetZhouxing Shi, Minlie Huang2020 · 30 citations
  3. From Individual to Common: An Early Exploration of Consensus in Non-verifiable Data for Balanced Preference OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Shangjian Yin, Zhouxing Shi2026 · 1 citation
  4. PII-VisBench: Evaluating Personally Identifiable Information Safety in Vision Language Models Along a Continuum of VisibilityFindings of the Association for Computational LinguisticsG M Shahariar, Zabir Al Nazi, Md Olid Hasan Bhuiyan, et al.2026

 Co-authors here

6 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 41 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

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