题　目： A General Method for Detecting Information Generated by Large Language Models

嘉　宾：Minjia Mao（毛敏加）, Ph.D. Candidate，University of Delaware

主持人：韩雪雯，助理教授，上海交通大学安泰经济与管理学院

时　间：2026年11月24日（周二）09:00-10:30

地　点：上海交通大学安泰经济与管理学院包兆龙图书馆A511

内容简介:

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.

演讲人简介：

Minjia Mao is a fifth-year Ph.D. candidate at the Institute for Financial Services Analytics, University of Delaware. His research focuses on developing novel and rigorous machine learning methods to safeguard business and society against the potential harms of large language models (LLMs), with particular interests in misinformation, AI alignment, and the foundations of LLMs. His work has been accepted for publication in Management Science, is under revision for a third-round review at MIS Quarterly, and has appeared in leading AI conferences, including ACL, COLM, and KDD. Through his research, he aims to make methodological contributions to both Information Systems and machine learning.