LLMs助人批量制造假新闻，检测难度大幅上升
Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?
- 用LLM辅助创作假新闻，人类与模型协作生成
- 真实新闻检测中LLM准确率比人高68%
- 假新闻检测中人机表现相当，均约60%准确
随着大型语言模型（LLMs）大规模生成内容，人们对虚假新闻传播的担忧加剧。LLMs生成逼真假新闻的能力对人工和自动化检测系统构成新挑战。本文基于一场高校竞赛，研究人类如何利用LLM制作假新闻，并评估人类标注者与AI模型的检测能力。共110名参与者使用LLM生成252篇独特假新闻，84名标注者参与检测任务。结果显示，对于真实新闻检测，LLM比人类高出约68%准确率；而假新闻检测中，人机表现相近，准确率均约为60%。此外，研究还分析了视觉元素（如图片）对假新闻检测准确性的影响，以及创作者提升内容可信度的多种策略。该工作揭示了在人机协作环境下，检测AI生成假新闻的复杂性正日益增加。
原文摘要 · Abstract (English)
With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings from a university-level competition that aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.
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