--- title: "Who cites On the Reliability of Watermarks — 81 citing papers" description: "81 papers of the Elamur Research library cite «On the Reliability of Watermarks for Large Language Models», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [On the Reliability of Watermarks for Large…](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/) / cited by # Who cites «On the Reliability of Watermarks for Large Language Models» 81 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← back to «On the Reliability of Watermarks for Large Language Models»](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/) 1. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly arXiv:2312.02003**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/) 2. [**"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models arXiv:2308.03825**Xinyue Shen, Zeyuan Chen, Michael Backes, et al.*2023 · 329 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/) 3. **[Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://md.research.elamur.ai/free/en/papers/krishna-2023-paraphrasing-evades-detectors-ai-generated/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.*2023 · 310 citations* 4. [**Can AI-Generated Text be Reliably Detected? arXiv:2303.11156**Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, et al.*2023 · 306 citations*](https://md.research.elamur.ai/free/en/papers/sadasivan-2023-ai-generated-text-reliably-detected/) 5. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 6. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations* 7. [**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/) 8. [**Baseline Defenses for Adversarial Attacks Against Aligned Language Models arXiv:2309.00614**Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.*2023 · 236 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/) 9. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) 10. **[Who Wrote this Code? Watermarking for Code Generation](https://md.research.elamur.ai/free/en/papers/lee-2024-who-wrote-this-code-watermarking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taehyun Lee, Seokhee Hong, Jaewoo Ahn, et al.*2024 · 103 citations* 11. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/) 12. **[Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?](https://md.research.elamur.ai/free/en/papers/pan-2025-can-llm-watermarks-robustly-prevent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leyi Pan, Aiwei Liu, Shiyu Huang, et al.*2025 · 11 citations* 13. **[Corpus and Baselines for Distinguishing Authentic, AI-Generated, and AI-Enhanced Resumes](https://md.research.elamur.ai/free/en/papers/loizidou-2026-corpus-baselines-distinguishing-authentic-ai/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Andrea Loizidou, Anshu Kiran Sharma, Adrian Esquivel, et al.*2026* 14. **[Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases](https://md.research.elamur.ai/free/en/papers/yang-2026-knowledge-infused-multi-bit-watermarking/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ke Yang, Shuguang Yuan, Jing Yu, et al.*2026* 15. **[LR-DWM: Efficient Watermarking for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/raban-2026-lr-dwm-efficient-watermarking-diffusion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ofek Raban, Gal Chechik, Ethan Fetaya*2026* 16. **[QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs](https://md.research.elamur.ai/free/en/papers/zhu-2026-quantilemark-message-symmetric-multi-bit/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junlin Zhu, Baizhou Huang, Xiaojun Wan*2026* 17. **[SWAN: Semantic Watermarking with Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/ye-2026-swan-semantic-watermarking-abstract-meaning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziping Ye, Gourab Dey, Christos Christodoulopoulos, et al.*2026* 18. **[The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks](https://md.research.elamur.ai/free/en/papers/zhao-2026-mark-fades-adaptive-evolutionary-paraphrase/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yusheng Zhao, Jian Zhao, Tianle Zhang, et al.*2026* 19. **[XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts](https://md.research.elamur.ai/free/en/papers/xu-2026-xmark-reliable-multi-bit-watermarking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiahao Xu, Rui Hu, Olivera Kotevska, et al.*2026* 20. **[You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking](https://md.research.elamur.ai/free/en/papers/yang-2026-you-can-have-second-chance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ke Yang, Dongyang Liang, Jing Yu, et al.*2026* 61 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/) — On the Reliability of Watermarks for Large Language Models