Anshuman Suri
Anshuman Suri
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Do Membership Inference Attacks Work on Large Language Models?
A large-scale evaluation of membership inference attacks (MIAs) on LLMs shows that MIAs perform barely better than random guessing, attributed to large datasets, few training iterations, and fuzzy boundaries between data members.
Michael Duan
,
Anshuman Suri
,
Niloofar Mireshghallah
,
Sewon Min
,
Weijia Shi
,
Luke Zettlemoyer
,
Yulia Tsvetkov
,
Yejin Choi
,
David Evans
,
Hannaneh Hajishirzi
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SoK: Memorization in General-Purpose Large Language Models
We explore the memorization capabilities of Large Language Models (LLMs), categorizing them into six types, and discuss their implications and challenges.
Valentin Hartmann
,
Anshuman Suri
,
Vincent Bindschaedler
,
David Evans
,
Shruti Tople
,
Robert West
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Subject Membership Inference Attacks in Federated Learning
We propose a notion of neuron sensitivity in terms of adversarial robustness, along with an attack that works as well as PGD. The notion can be extended as a regularization term, providing adversarial robustness without adversarial training.
Anshuman Suri
,
Pallika Kanani
,
Virendra J. Marathe
,
Daniel W. Peterson
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One Neuron to Fool Them All
We propose a notion of neuron sensitivity in terms of adversarial robustness, along with an attack that works as well as PGD. The notion can be extended as a regularization term, providing adversarial robustness without adversarial training.
Anshuman Suri
,
David Evans
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