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Large language models (LLMs) are considered valuable Intellectual Properties (IP) for legitimate owners due to the enormous computational cost of training.
Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh. 2017 · 2017
Earlier work this paper cites.
Digital watermarking for deep neural networks
Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa, and Shin’ichi Satoh. 2018 · 2018
Earlier work this paper cites.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, and 12 others. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Earlier work this paper cites.
Radioactive data: Tracing through training
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Hervé Jégou. 2020 · 2020
Earlier work this paper cites.
You are caught stealing my winning lottery ticket! making a lottery ticket claim its ownership
Xuxi Chen, Tianlong Chen, Zhenyu Zhang, and Zhangyang Wang. 2021 · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Riga: Covert and robust white-box watermarking of deep neural networks
Tianhao Wang and Florian Kerschbaum. 2021 · 2021
Earlier work this paper cites.
Copy, right? a testing framework for copyright protection of deep learning models
Jialuo Chen, Jingyi Wang, Tinglan Peng, Youcheng Sun, Peng Cheng, Shouling Ji, Xingjun Ma, Bo Li, and Dawn Song. 2022 · 2022
Earlier work this paper cites.
Learning fair graph neural networks with limited and private sensitive attribute information
Enyan Dai and Suhang Wang. 2022 · 2022
Earlier work this paper cites.
Are you stealing my model? sample correlation for fingerprinting deep neural networks
Jiyang Guan, Jian Liang, and Ran He. 2022 · 2022
Earlier work this paper cites.
Defending against model stealing via verifying embedded external features
Yiming Li, Linghui Zhu, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, and Xiaochun Cao. 2022 · 2022
Cited alongside, same era.
Your model trains on my data? protecting intellectual property of training data via membership fingerprint authentication
Gaoyang Liu, Tianlong Xu, Xiaoqiang Ma, and Chen Wang. 2022 · 2022
Cited alongside, same era.
Non-transferable learning: A new approach for model ownership verification and applicability authorization
Lixu Wang, Shichao Xu, Ruiqi Xu, Xiao Wang, and Qi Zhu. 2022 · 2022
Cited alongside, same era.
Palm 2 technical report
Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, and 1 others. 2023 · 2023
Cited alongside, same era.
Three bricks to consolidate watermarks for large language models
Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon. 2023 · 2023
Cited alongside, same era.
Gpt-4 technical report
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, and 1 others. 2024 · 2024
Later among the works it cites.
Llmmap: Fingerprinting for large language models
Dario Pasquini, Evgenios M. Kornaropoulos, and Giuseppe Ateniese. 2024 · 2024
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
Later among the works it cites.
Hey, that’s my model! introducing chain & hash, an llm fingerprinting technique
Mark Russinovich and Ahmed Salem. 2024 · 2024
Later among the works it cites.
MMLU-pro: A more robust and challenging multi-task language understanding benchmark
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. 2023 · 2023
Cited alongside, same era.
Deep intellectual property protection: A survey
Yuchen Sun, Tianpeng Liu, Panhe Hu, Qing Liao, Shaojing Fu, Nenghai Yu, Deke Guo, Yongxiang Liu, and Li Liu. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, and 1 others. 2023 · 2023
Cited alongside, same era.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson. 2023 · 2023
Cited alongside, same era.
Hide and seek: Fingerprinting large language models with evolutionary learning
Dmitri Iourovitski, Sanat Sharma, and Rakshak Talwar. 2024 · 2024
Cited alongside, same era.
Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang. 2024 · 2024
Cited alongside, same era.
Instructional fingerprinting of large language models
Jiashu Xu, Fei Wang, Mingyu Ma, Pang Wei Koh, Chaowei Xiao, and Muhao Chen. 2024 · 2024
Later among the works it cites.
A fingerprint for large language models
Zhiguang Yang and Hanzhou Wu. 2024 · 2024
Later among the works it cites.
MAmmoTH: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. 2024 · 2024
Later among the works it cites.
Pregip: Watermarking the pretraining of graph neural networks for deep ip protection
Enyan Dai, Minhua Lin, and Suhang Wang. 2025 · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, and 1 others. 2025 · 2025
Closest in time.
Stealing training graphs from graph neural networks
Minhua Lin, Enyan Dai, Junjie Xu, Jinyuan Jia, Xiang Zhang, and Suhang Wang. 2025 · 2025
Closest in time.
REEF: Representation encoding fingerprints for large language models
Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, and Jing Shao. 2025 · 2025
Closest in time.