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 that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.

本文利用威胁模型，展示公开可获得的语言模型能够可靠地生成偏见新闻内容，并使用可控文本生成生成大量高质量的偏见新闻文章。通过80个参与者的主观评价，证明所生成的偏见新闻通常是流畅的；通过24名参与者的偏见评估，证明所生成文章的偏见（左或右）通常是明显的，可以轻易地被识别。

使用自然语言模型生成偏见新闻的威胁