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The widespread use of Large Language Models (LLMs) in society creates new information security challenges for developers, organizations, and end-users alike.
Connectionism and cognitive architecture: A critical analysis
Jerry A. Fodor and Zenon W. Pylyshyn · 1988
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Efficient text searching
Ricardo Baeza-Yates · 1989
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Fast text searching: allowing errors
Sun Wu and Udi Manber · 1992
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Language models are few-shot learners, 2020
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, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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Label-only membership inference attacks, 2021
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2007
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Natural Language Processing with Python
Steven Bird, Edward Loper, and Ewan Klein · 2009
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Membership inference attacks against machine learning models, 2017
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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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 Xiaodong Song · 2018
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Xiaodong Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2020
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The pile: An 800gb dataset of diverse text for language modeling, 2020
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
Cited alongside, same era.
Membership inference attacks from first principles, 2022
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
Cited alongside, same era.
Quantifying privacy risks of masked language models using membership inference attacks
Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, and Tao Jiang · 2023
Later among the works it cites.
A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang · 2024
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Compositional learning of functions in humans and machines, 2024
Yanli Zhou, Brenden M. Lake, and Adina Williams · 2024
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Imposter.ai: Adversarial attacks with hidden intentions towards aligned large language models, 2024
Xiao Liu, Liangzhi Li, Tong Xiang, Fuying Ye, Lu Wei, Wangyue Li, and Noa Garcia · 2024
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Github copilot: Parrot or crow?, 2021
Albert Ziegler · 2024
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Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri · 2022
Cited alongside, same era.
Reconstructing training data with informed adversaries, 2022
Borja Balle, Giovanni Cherubin, and Jamie Hayes · 2022
Cited alongside, same era.
A survey of large language models, 2023
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
Cited alongside, same era.
Scalable extraction of training data from (production) language models, 2023
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee · 2023
Cited alongside, same era.
Quantifying memorization across neural language models, 2023
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2023
Cited alongside, same era.
Provable memorization capacity of transformers
Junghwan Kim, Michelle Kim, and Barzan Mozafari · 2023
Cited alongside, same era.
Closest in time.
Extracting (even more) training data from production language models, 2024
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, and Katherine Lee · 2024
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Natural language decomposition and interpretation of complex utterances, 2024
Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Ben Van Durme · 2024
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Jailbreaking leading safety-aligned llms with simple adaptive attacks, 2024
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
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