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Viable Threat on News Reading: Generating Biased News Using Natural Language Models
[article]
2020
arXiv
pre-print
Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader's behavior and influence their bias. In this paper, we use a threat model to demonstrate
arXiv:2010.02150v1
fatcat:tbboz4mrb5cpriipqamzwoobmi