Zhexin Zhang
Zhexin Zhang has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Bias, fairness & safety; often with Minlie Huang, Pei Ke, Fei Mi.
OpenAlex · ORCID · h-index 11 · 58 works · 413 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 313 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4105
Affiliations
Concepts, shelves and topics
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 work
🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 3 worksLanguage models & pretraining · 3 works
Works by year
2020s
Papers
- SafetyBench: Evaluating the Safety of Large Language Models (2023 · 136 citations)
Assessing LLM safety is difficult. Earlier resources usually target a single risk such as toxicity or bias, and recent broad Chinese benchmarks cover only one language. Judging free-form outputs also requires either costly human raters or imperfect automatic classifiers. SafetyBench avoids the judging problem by… - Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization (2024 · 112 citations)
The paper argues that jailbreaking attacks on LLMs succeed because models are tuned for two competing objectives, helpfulness and safety, without being told which one wins when they clash. - Unveiling the Implicit Toxicity in Large Language Models (2023 · 71 citations)
Most red-teaming work on large language models looks for overtly abusive outputs, which standard toxicity detectors catch easily.
All works
- SafetyBench: Evaluating the Safety of Large Language ModelsarXiv:2309.07045Zhexin Zhang, Leqi Lei, Lindong Wu, et al.2023 · 136 citations
- Defending Large Language Models Against Jailbreaking Attacks Through Goal PrioritizationAnnual Meeting of the Association for Computational Linguistics (ACL)Zhexin Zhang, Junxiao Yang, Pei Ke, et al.2024 · 112 citations
- Safety Assessment of Chinese Large Language ModelsarXiv:2304.10436 · original ↗ · no summary yetHao Sun et al.2023 · 88 citations
- Unveiling the Implicit Toxicity in Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaxin Wen, Pei Ke, Hao Sun, et al.2023 · 71 citations
- How Should We Enhance the Safety of Large Reasoning Models: An Empirical StudyProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhexin Zhang et al.2026 · 5 citations
Co-authors here
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2024
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2023
Cited by
Updated
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