关键词: 大语言模型, 虚假信息检测, 数据增强, 关键信息抽取

Abstract: Globally, the spread of false information on the Internet, especially on social media, has become an urgent issue to be addressed. With the rise of artificial intelligence technology, the application research of large language models in false information detection has become a hot topic. However, in China, related research in this field is relatively scarce and has not yet formed a complete system. To systematically review the current research status and development trends, this paper provides a comprehensive summary of the application of large language models in false information detection, which is the first review in China on this topic. This paper focuses on the false information detection framework based on large language models and deeply explores the innovative applications of large language models in data generation, data augmentation, information extraction, integration with external knowledge and tools, model improvement, final fusion decision-making, explanation and feedback generation during the false information detection process. It outlines the definition of false information and the background of its spread, elaborates on the core detection process in the framework, sorts out the innovation points in each link of the false information detection framework, summarizes the "internal" and "external" detection processes, and expounds on the model improvements such as retrieval enhancement, prompt engineering, fine-tuning, and final decision-making involved in the detection process. Finally, it analyzes the challenges faced by false information detection based on large language models at present and looks forward to future research directions, with the aim of providing references and inspirations for the development of false information detection based on large language models.

Key words: Large Language Models, False Information Detection, Data Augmentation, Key Information Extraction