Before revision:  Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings .
After revision:  Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pretrained language models. Our experiments demonstrate a relationship between model parameters such as size and temperature and their propensity to generate conspiracy theory text. These results indicate the need for a more thorough review of NLG applications before release and an in-depth discussion of the drawbacks of memorization in generative language models .