--- title: "Who cites Safety Assessment of Chinese — 74 citing papers" description: "74 papers of the Elamur Research library cite «Safety Assessment of Chinese Large Language Models», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Safety Assessment of Chinese Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/) / cited by # Who cites «Safety Assessment of Chinese Large Language Models» 74 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «Safety Assessment of Chinese Large Language Models»](https://md.research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/) 1. **[BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 362 citations* 2. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/) 3. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/) 4. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) 5. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback arXiv:2310.12773**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/) 6. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations* 7. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts arXiv:2309.10253**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/) 8. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher arXiv:2308.06463**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/) 9. **[WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs](https://md.research.elamur.ai/free/en/papers/han-2024-wildguard-open-one-stop-moderation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Seungju Han, Kavel Rao, Allyson Ettinger, et al.*2024 · 165 citations* 10. [**SafetyBench: Evaluating the Safety of Large Language Models arXiv:2309.07045**Zhexin Zhang, Leqi Lei, Lindong Wu, et al.*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-safetybench-evaluating-safety-large/) 11. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) 12. **[SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-salad-bench-hierarchical-comprehensive-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lijun Li, Bowen Dong, Ruohui Wang, et al.*2024 · 119 citations* 13. **[R-Judge: Benchmarking Safety Risk Awareness for LLM Agents](https://md.research.elamur.ai/free/en/papers/yuan-2024-r-judge-benchmarking-safety-risk/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tongxin Yuan, Zhiwei He, Lingzhong Dong, et al.*2024 · 106 citations* 14. **[Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-not-all-countries-celebrate-thanksgiving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang, et al.*2024 · 96 citations* 15. **[Detoxifying Large Language Models via Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-detoxifying-large-language-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.*2024 · 84 citations* 16. **[Unveiling the Implicit Toxicity in Large Language Models](https://md.research.elamur.ai/free/en/papers/wen-2023-unveiling-implicit-toxicity-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Wen, Pei Ke, Hao Sun, et al.*2023 · 72 citations* 17. [**CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility arXiv:2307.09705**Guohai Xu, Jiayi Liu, Ming Yan, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-cvalues-measuring-values-chinese/) 18. [**SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety arXiv:2404.05399**Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-safetyprompts-systematic-review-open-datasets/) 19. [**CLEVA: Chinese Language Models EVAluation Platform arXiv:2308.04813**Yanyang Li, Jianqiao Zhao, Duo Zheng, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cleva-chinese-language-models/) 20. [**TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling arXiv:2503.18288**Cheng Huang, Fan Gao, Nyima Tashi, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/huang-2025-tfd-comprehensive-structured-tibetan-foundation/) 21. [**Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models arXiv:2412.15265**Yingshui Tan, Boren Zheng, Baihui Zheng, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/tan-2024-chinese-safetyqa-safety-short/) 22. **[SafeMT: Multi-turn Safety for Multimodal Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2026-safemt-multi-turn-safety-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Han Zhu, Juntao Dai, Jiaming Ji, et al.*2026 · 3 citations* 23. **[Beyond English and Evasion: A Human-Annotated Multi-Domain Benchmark for High-Stakes LLM Safety Evaluation in Chinese](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-beyond-english-evasion-human-annotated/) [Workshop on the Role of Resources in the Age of Large Language Models](https://md.research.elamur.ai/free/en/venues/resourceful/)**Wajdi Zaghouani, Kholoud Khalil Aldous, Yicheng Gao*2026 · 2 citations* 24. [**Methodology of Adapting Large English Language Models for Specific Cultural Contexts arXiv:2406.18192**Wenjing Zhang, Siqi Xiao, Xuejiao Lei, et al.*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-methodology-adapting-large-english-language/) 25. **[AlbanianLLMSafety: A Safety Evaluation Dataset for Large Language Models in Albanian](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-albanianllmsafety-safety-evaluation-dataset-large/) [SIGUL Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages](https://md.research.elamur.ai/free/en/venues/sigul/)**Wajdi Zaghouani, Kholoud Khalil Aldous, Isra Fejzullaj*2026 · 1 citation* 49 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/) — Safety Assessment of Chinese Large Language Models