Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Author:

Crothers Evan N.1ORCID,Japkowicz Nathalie2ORCID,Viktor Herna L.1ORCID

Affiliation:

1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

2. Department of Computer Science, American University, Washington, DC, USA

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference204 articles.

1. Exploring the limits of transfer learning with a unified text-to-text transformer;raffel;J Mach Learn Res,2020

2. Adversarial attacks and defense on texts: A survey;huq;arXiv 2005 14108,2020

3. Generation of formal and informal sentences;sheikha;Proc 13th Eur Workshop Natural Lang Gener,2011

4. fairseq: A Fast, Extensible Toolkit for Sequence Modeling

5. Fooling MOSS Detection with Pretrained Language Models

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