Large Language Models Can Be Strong Differentially Private Learners

Large Language Models Can Be Strong Differentially Private Learners

12 October 2021

Florian Tramèr

Tatsunori Hashimoto

Papers citing "Large Language Models Can Be Strong Differentially Private Learners"

9 / 59 papers shown

Title
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,843 0 18 Apr 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 91 110 0 25 Feb 2021
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics Sebastian Gehrmann Tosin P. Adewumi Karmanya Aggarwal Pawan Sasanka Ammanamanchi Aremu Anuoluwapo ... Nishant Subramani Wei-ping Xu Diyi Yang Akhila Yerukola Jiawei Zhou VLM 246 285 0 02 Feb 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,916 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 267 1,812 0 14 Dec 2020
Private Post-GAN Boosting Marcel Neunhoeffer Zhiwei Steven Wu Cynthia Dwork 114 29 0 23 Jul 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 408 2,584 0 03 Sep 2019
Efficient Per-Example Gradient Computations Ian Goodfellow 184 74 0 07 Oct 2015