학술논문

TMLab: Generative Enhanced Model (GEM) for adversarial attacks
Document Type
Working Paper
Source
Subject
Computer Science - Computation and Language
Computer Science - Machine Learning
Language
Abstract
We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT-2 architecture. The addition of novel target vocabulary input to the already existing context input enabled controlled text generation. The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control. As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.
Comment: 7 pages + appendix