论文标题
机器生成的文本：威胁模型和检测方法的全面调查
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
论文作者
论文摘要
机器生成的文本越来越难以区分人类的文本。强大的开源型号是免费的，并且用户友好的工具使对生成模型的访问的访问正在激增。在这项调查的第一版之后不久发布的Chatgpt代表了这些趋势。最先进的自然语言生成（NLG）系统的巨大潜力被滥用的众多途径所抑制。检测机器生成的文本是减少NLG模型滥用的关键对策，该模型面临重大的技术挑战和许多开放问题。我们提供了一项调查，包括1）对当代NLG系统提出的威胁模型的广泛分析，以及2）对机器生成的文本检测方法的最完整审查。该调查将机器生成的文本置于其网络安全和社会环境之内，并为未来的工作提供了强有力的指导，以解决最关键的威胁模型，并确保检测系统本身通过公平，稳健性和问责制表现出可信度。
Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.
