Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation. In light of these concerns, several studies have proposed to detect machine-generated fake news by capturing their stylistic differences from human-written text. These approaches, broadly termed stylometry, have found success in source attribution and misinformation detection in human-written texts. However, in this work, we show that stylometry is limited against machine-generated misinformation. While humans speak differently when trying to deceive, LMs generate stylistically consistent text, regardless of underlying motive. Thus, though stylometry can successfully prevent impersonation by identifying text provenance, it fails to distinguish legitimate LM applications from those that introduce false information. We create two benchmarks demonstrating the stylistic similarity between malicious and legitimate uses of LMs, employed in auto-completion and editing-assistance settings.1 Our findings highlight the need for non-stylometry approaches in detecting machinegenerated misinformation, and open up the discussion on the desired evaluation benchmarks.

中文翻译：

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Stylometry 在检测机器生成的假新闻方面的局限性

神经语言模型 (LM) 的最新发展引起了人们对其潜在误用以自动传播错误信息的担忧。鉴于这些担忧，一些研究提议通过捕捉机器生成的假新闻与人类书面文本的风格差异来检测它们。这些方法，广义上称为文体法，已经在人类书面文本中的来源归因和错误信息检测方面取得了成功。然而，在这项工作中，我们表明文体法对机器生成的错误信息是有限的。虽然人类在试图欺骗时说话方式不同，但 LM 会生成风格一致的文本，而不管潜在动机如何。因此，尽管文体法可以通过识别文本出处成功地防止冒充，它无法区分合法的 LM 应用程序和那些引入虚假信息的应用程序。我们创建了两个基准来证明 LM 的恶意和合法使用之间的风格相似性，用于自动完成和编辑辅助设置。 1 我们的发现强调了在检测机器生成的错误信息方面对非风格测量方法的需求，并开启了关于期望的评估基准。