Abstract
The proliferation of large language models (LLMs) has significantly transformed the digital information landscape, making it increasingly challenging to distinguish between human-written and LLM-generated content. Detecting LLM-generated information is essential for preserving trust on digital platforms (e.g., social media and e-commerce sites) and preventing the spread of misinformation, a topic that has garnered significant attention in IS research. However, current detection methods, which primarily focus on identifying content generated by specific LLMs in known domains, face challenges in generalizing to new (i.e., unseen) LLMs and domains. This limitation reduces their effectiveness in real-world applications, where the number of LLMs is rapidly multiplying and content spans a vast array of domains. In response, we introduce a general LLM detector (GLD) that combines a twin memory networks design and a theory-guided detection generalization module to detect LLM-generated information across unseen LLMs and domains. Using real-world datasets, we conduct extensive empirical evaluations and case studies to demonstrate the superiority of GLD over state-of-the-art detection methods. The study has important academic and practical implications for digital platforms and LLMs.
Abstract (translated)
大型语言模型（LLMs）的广泛使用显著改变了数字信息景观，使得区分人类撰写的内容和由LLM生成的内容变得越来越困难。检测LLM生成的信息对于维护数字平台（如社交媒体和电子商务网站）上的信任以及防止错误信息的传播至关重要，这是一个在信息系统研究中备受关注的话题。然而，目前主要专注于识别特定LLM在其已知领域内生成内容的方法，在泛化到新的（即未知的）LLM和领域时面临挑战。这种限制降低了它们在现实世界应用中的有效性，因为在这些应用中，LLMs的数量正在迅速增加，并且内容涵盖了广泛的领域。 为了解决这个问题，我们引入了一种通用的大语言模型检测器（GLD），该检测器结合了双内存网络设计和理论指导的检测泛化模块来检测跨越未知LLM和领域的LLM生成的信息。通过使用真实世界的数据集进行广泛的实证评估和案例研究，我们展示了GLD在与现有最先进的检测方法相比时的优势。这项研究对于数字平台和大型语言模型都具有重要的学术和实践意义。
URL
https://arxiv.org/abs/2506.21589
