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In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics.
Simple demographics often identify people uniquely
Latanya Sweeney · 2000
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Enhancing authorship attribution by utilizing syntax tree profiles
Michael Tschuggnall and Günther Specht · 2014
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Regulation (EU) 2016/679 of the European Parliament and of the Council
European Parliament and Council of the European Union · 2016
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Reynier Ortega-Bueno, Berta Chulvi, Francisco Rangel, Paolo Rosso, and Elisabetta Fersini · 2021
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Francisco Rangel, Gretel Liz de la Peña-Sarracén, María Alberta Chulvi-Ferriols, Elisabetta Fersini, and Paolo Rosso · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 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 · 2022
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The text anonymization benchmark (tab): A dedicated corpus and evaluation framework for text anonymization
Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet · 2022
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Dp-vae: Human-readable text anonymization for online reviews with differentially private variational autoencoders
Benjamin Weggenmann, Valentin Rublack, Michael Andrejczuk, Justus Mattern, and Florian Kerschbaum · 2022
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu · 2023
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Seth Neel and Peter Chang · 2023
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Experimental evidence on the productivity effects of generative artificial intelligence
Shakked Noy and Whitney Zhang · 2023
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Beyond memorization: Violating privacy via inference with large language models
Robin Staab, Mark Vero, Mislav Balunović, and Martin Vechev · 2023
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A short survey of viewing large language models in legal aspect
Zhongxiang Sun · 2023
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Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
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Chatgpt has been turned into a social media surveillance assistant, november 2023
Thomas Brewster · 2023
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Reducing privacy risks in online self-disclosures with language models
Yao Dou, Isadora Krsek, Tarek Naous, Anubha Kabra, Sauvik Das, Alan Ritter, and Wei Xu · 2023
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Daniel Yacob Espinosa and Grigori Sidorov · 2023
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Timour Igamberdiev and Ivan Habernal · 2023
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Yansong Li, Zhixing Tan, and Yang Liu · 2023
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Autoattacker: A large language model guided system to implement automatic cyber-attacks
Jiacen Xu, Jack W Stokes, Geoff McDonald, Xuesong Bai, David Marshall, Siyue Wang, Adith Swaminathan, and Zhou Li · 2024
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