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We introduce Many-Shot Regurgitation (MSR) prompting, a new black-box membership inference attack framework for examining verbatim content reproduction in large language models (LLMs).
Sulla determinazione empirica di una legge didistribuzione
Kolmogorov An · 1933
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Use of ranks in one-criterion variance analysis
William H Kruskal and W Allen Wallis · 1952
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Dominance statistics: Ordinal analyses to answer ordinal questions
Norman Cliff · 1993
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Extracting training data from large language models
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Training language models to follow instructions with human feedback
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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The privacy onion effect: Memorization is relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, and Florian Tramer · 2022
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An empirical analysis of memorization in fine-tuned autoregressive language models
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David K Evans, and Taylor Berg-Kirkpatrick · 2022
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J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, L Fedus, L Metz, M Pokorny, et al · 2022
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Quantifying privacy risks of masked language models using membership inference attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri · 2022
Cited alongside, same era.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
Jailbreaking black box large language models in twenty queries, 2023
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong · 2023
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Low-cost high-power membership inference by boosting relativity
Sajjad Zarifzadeh, Philippe Cheng-Jie Marc Liu, and Reza Shokri · 2023
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Proving test set contamination in black box language models
Yonatan Oren, Nicole Meister, Niladri Chatterji, Faisal Ladhak, and Tatsunori B Hashimoto · 2023
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Did the neurons read your book? document-level membership inference for large language models
Matthieu Meeus, Shubham Jain, Marek Rei, and Yves-Alexandre de Montjoye · 2023
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer · 2023
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Gemini: a family of highly capable multimodal models
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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
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Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
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Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, and Tao Jiang · 2023
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Privacy auditing with one (1) training run
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Machine unlearning of pre-trained large language models
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