  With the growing adoption of text generation models in today's society, users
are increasingly exposed to machine-generated text. This in turn can leave
users vulnerable to the generation of harmful information such as conspiracy
theories. While the propagation of conspiracy theories through social media has
been studied, previous work has not evaluated their diffusion through text
generation. In this work, we investigate the propensity for language models to
generate conspiracy theory text. Our study focuses on testing these models for
the elicitation of conspiracy theories and comparing these generations to
human-written theories from Reddit. We also introduce a new dataset consisting
of conspiracy theory topics, machine-generated conspiracy theories, and
human-written conspiracy theories. 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.
