Fetching the paper…
Reading the bibliography…
Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model's training set.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
Earlier work this paper cites.
The physical interpretation of the quantum dynamics
P. A. M. Dirac · 1927
Earlier work this paper cites.
On a method of determining whether a sample of size n supposed to have been drawn from a parent population having a known probability integral has probably been drawn at random
Karl Pearson · 1933
Earlier work this paper cites.
The redundancy of english
Claude E Shannon · 1951
Earlier work this paper cites.
Distributional structure, 1954
ZS Harris · 1954
Earlier work this paper cites.
Statistical methods for research workers
Ronald Aylmer Fisher · 1970
Earlier work this paper cites.
An additive method for combining probability values from independent experiments
Eugene S Edgington · 1972
Earlier work this paper cites.
Perplexity—a measure of the difficulty of speech recognition tasks
Fred Jelinek, Robert L Mercer, Lalit R Bahl, and James K Baker · 1977
Earlier work this paper cites.
The logit statistic for combining probabilities-an overview
Govind S Mudholkar and EO George · 1979
Earlier work this paper cites.
A technique for high-performance data compression
Terry A. Welch · 1984
Earlier work this paper cites.
A regularity statistic for medical data analysis
Steven M Pincus, Igor M Gladstone, and Richard A Ehrenkranz · 1991
Earlier work this paper cites.
zlib compressed data format specification version 3.3
Paul Deutsch and Jean-Loup Gailly · 1996
Earlier work this paper cites.
Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich · 2015
Earlier work this paper cites.
Regulation (EU) 2016/679 of the European Parliament and of the Council
European Parliament and Council of the European Union · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu · 2016
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Choosing between methods of combining-values
Nicholas A Heard and Patrick Rubin-Delanchy · 2018
Cited alongside, same era.
Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
Later among the works it cites.
Quantifying privacy risks of masked language models using membership inference attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri · 2022
Later among the works it cites.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
Later among the works it cites.
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
Later among the works it cites.
Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
A pragmatic approach to membership inferences on machine learning models
Yunhui Long, Lei Wang, Diyue Bu, Vincent Bindschaedler, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen · 2020
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Cited alongside, same era.
Quantifying the privacy risks of learning high-dimensional graphical models
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos · 2021
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
Cited alongside, same era.
Later among the works it cites.
Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
Later among the works it cites.
Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi · 2024
Closest in time.
Blind baselines beat membership inference attacks for foundation models
Debeshee Das, Jie Zhang, and Florian Tramèr · 2024
Closest in time.
Low-cost high-power membership inference attacks
Sajjad Zarifzadeh, Philippe Liu, and Reza Shokri · 2024
Closest in time.
New york times sues openai, microsoft for copyright infringement
Eileen AJ Connelly · 2024
Closest in time.
Min-k%++: Improved baseline for detecting pre-training data from large language models
Jingyang Zhang, Jingwei Sun, Eric Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao Yang, and Hai Li · 2024
Closest in time.
Mosh Levy, Alon Jacoby, and Yoav Goldberg · 2024
Closest in time.
Inherent challenges of post-hoc membership inference for large language models
Matthieu Meeus, Shubham Jain, Marek Rei, and Yves-Alexandre de Montjoye · 2024
Closest in time.