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 mediahas 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 .
The adoption of natural language generation (NLG) models can leave individuals vulnerable to the generation of harmful information memorized by the models, such as conspiracy theories. While previous studies examine conspiracy theories in the context of social media, they have not evaluated their presence in the new space of generative language models . In this work, we investigate the capability of language models to generate conspiracy theory text. Specifically, we aim to answer: can we test pretrained generative language models for the memorization and elicitation of conspiracy theories without access to the model's training data? We highlight the difficulties of this task and discuss it in the context of memorization, generalization, and hallucination. Utilizing a new dataset consisting of conspiracy theory topics and machine-generated conspiracy theories helps us discover that many conspiracy theories 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 .
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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
The adoption of natural language generation (NLG) models can leave individuals
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memorized by the models,
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the propagation of conspiracy theories through social mediahas been studied, previous work has
previous studies examine conspiracy theories in the context of social media, they have
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diffusion through text generation
presence in the new space of generative language models
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propensity for
capability of
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Our study focuses on testing these
Specifically, we aim to answer: can we test pretrained generative language
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memorization and
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and comparing these generations to human-written theories from Reddit. We also introduce
without access to the model's training data? We highlight the difficulties of this task and discuss it in the context of memorization, generalization, and hallucination. Utilizing
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,
and
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, and human-written conspiracy theories. Our experiments show that many well-known conspiracy theory topics
helps us discover that many conspiracy theories
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pre-trained language models, and can become more prevalent through different model settings
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