Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships

19 February 2024

Papers citing "Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships"

8 / 8 papers shown

Title
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 319 11,953 0 04 Mar 2022
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 242 593 0 14 Jul 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 270 1,996 0 31 Dec 2020
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,815 0 14 Dec 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 358 0 24 Mar 2020
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 72 134 0 10 Mar 2020
Revisiting Self-Training for Neural Sequence Generation Junxian He Jiatao Gu Jiajun Shen MarcÁurelio Ranzato SSL LRM 244 269 0 30 Sep 2019
PubMedQA: A Dataset for Biomedical Research Question Answering Qiao Jin Bhuwan Dhingra Zhengping Liu William W. Cohen Xinghua Lu 222 812 0 13 Sep 2019