Language generation models' democratization benefits many domains, from
answering health-related questions to enhancing education by providing
AI-driven tutoring services. However, language generation models'
democratization also makes it easier to generate human-like text at-scale for
nefarious activities, from spreading misinformation to targeting specific
groups with hate speech. Thus, it is essential to understand how people
interact with bots and develop methods to detect bot-generated text. This paper
shows that bot-generated text detection methods are more robust across datasets
and models if we use information about how people respond to it rather than
using the bot's text directly. We also analyze linguistic alignment, providing
insight into differences between human-human and human-bot conversations.

本文研究了语言生成模型的民主化对人们与机器人互动方式以及识别机器人生成的文本方法的影响，并发现结合人类对话反馈信息的机器人生成文本识别方法更加稳健，同时对人机会话的语言统计学变化进行了分析。