论文标题
使用变压器自动识别自动生成的头条
Identifying Automatically Generated Headlines using Transformers
论文作者
论文摘要
通过互联网和社交媒体传播的虚假信息会影响公众舆论和用户活动，而生成模型则可以比以前更快地生成伪造的内容。在不久的将来，识别深度学习模型产生的假内容将在保护用户免受错误信息方面发挥关键作用。为此，创建了一个包含人类和计算机生成的头条新闻的数据集，用户研究表明，人类只能在47.8％的情况下识别假头条。但是，最准确的自动方法是，变压器达到了85.7％的总体精度，表明可以准确过滤从语言模型产生的内容。
False information spread via the internet and social media influences public opinion and user activity, while generative models enable fake content to be generated faster and more cheaply than had previously been possible. In the not so distant future, identifying fake content generated by deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8% of the cases. However, the most accurate automatic approach, transformers, achieved an overall accuracy of 85.7%, indicating that content generated from language models can be filtered out accurately.
