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With the rise of large language models (LLMs), increasing research has recognized their risk of leaking personally identifiable information (PII) under malicious attacks.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Introducing the enron corpus
Bryan Klimt and Yiming Yang · 2004
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Acquired amnesia in childhood: A single case study
Stefano Vicari, Deny Menghini, Margherita Di Paola, Laura Serra, Alberto Donfrancesco, Paola Fidani, Giuseppe Maria Milano, and Giovanni Augusto Carlesimo · 2007
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Anterograde amnesia
Hans J Markowitsch · 2008
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Clair collection of fraud email, acl data and code repository
Dragomir Radev · 2008
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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This email could save your life: Introducing the task of email subject line generation
Rui Zhang and Joel Tetreault · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Similarity based information retrieval using levenshtein distance algorithm
Daw Khin Po · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov · 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
Cited alongside, same era.
Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
Cited alongside, same era.
Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2022
Cited alongside, same era.
Differentially private decoding in large language models
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, and Richard Zemel · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Detecting and redacting pii using amazon comprehend, 2024
Amazon Web Services Comprehend · 2024
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Detecting location using amazon location, 2024
Amazon Web Services Location · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Perplexity - transformers documentation, 2024
Hugging Face · 2024
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Propile: Probing privacy leakage in large language models
Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh · 2024
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
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Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Just fine-tune twice: Selective differential privacy for large language models
Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, and Zhou Yu · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
Cited alongside, same era.
Potsawee Manakul, Adian Liusie, and Mark JF Gales · 2023
Cited alongside, same era.
Can sensitive information be deleted from llms? objectives for defending against extraction attacks
Vaidehi Patil, Peter Hase, and Mohit Bansal · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Cited alongside, same era.
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer · 2024
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Llama-2-7b-hf
Meta-Llama · 2024
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Presidio research
Microsoft · 2024
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Gpt-4o, 2024
OpenAI · 2024
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Judging the judges: Evaluating alignment and vulnerabilities in llms-as-judges
Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally, Sankaran Vaidyanathan, and Dieuwke Hupkes · 2024
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Replacing judges with juries: Evaluating llm generations with a panel of diverse models
Pat Verga, Sebastian Hofstatter, Sophia Althammer, Yixuan Su, Aleksandra Piktus, Arkady Arkhangorodsky, Minjie Xu, Naomi White, and Patrick Lewis · 2024
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Selective forgetting: Advancing machine unlearning techniques and evaluation in language models
Lingzhi Wang, Xingshan Zeng, Jinsong Guo, Kam-Fai Wong, and Georg Gottlob · 2024
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Machine unlearning of pre-trained large language models
Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, and Xiang Yue · 2024
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Towards building the federatedgpt: Federated instruction tuning
Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Tong Yu, Guoyin Wang, and Yiran Chen · 2024
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