基于大语言模型的虚假新闻检测研究

Research on fake news detection based on large language model

虚假新闻在网络空间迅速蔓延,严重威胁公众的健康与安全。为有效应对这一挑战,研究者开始探索基于机器学习算法的虚假新闻检测方法,旨在探讨大型语言模型在虚假新闻检测中的应用及其有效性。研究目标是开发一种能够自动识别和分类新闻真实性的系统,以减少虚假信息的传播。其中,采用二分类方法评估新闻的真实性,分为“真实新闻”和“虚假新闻”2类。在方法上,利用多个常用的LLM平台,通过API调用示例展示如何集成这些工具。此外,使用函数调用来搜索引擎,同时采用RAG(Retrieval Augmented Generation)框架,这是一种结合检索与生成的方法,通过检索外部知识辅助生成准确的回答。提供了一系列示例,包括直接询问法、来源可靠性评估、误导性陈述识别、深度分析、引用与参考检查以及综合评价,展示如何评估新闻的可信度。结果表明,通过提示词工程(prompt engineering),可以有效指导模型完成特定任务,如文本分类、问答和文本生成等;结合RAG框架和提示词工程的大型语言模型,能够显著提高假新闻检测的准确性与效率。

Fake news is rapidly spreading in cyberspace,posing a serious threat to public health and safety.To effectively address this challenge,researchers have begun exploring fake news detection methods based on machine learning algorithms,aiming to investigate the application and effectiveness of large scale language models in fake news detection.The research objective is to develop a system that can automatically identify and classify the authenticity of news,in order to reduce the spread of false information.Among them,the binary classification method is used to evaluate the authenticity of news,which is divided into two categories:“real news”and“fake news”.In terms of methodology,utilize multiple commonly used LLM platforms and demonstrate how to integrate these tools through API call examples.In addition,function calls are used to search engines,and the RAG(Retrieval Augmented Generation)framework is adopted,which is a method that combines retrieval and generation to assist in generating accurate answers by retrieving external knowledge.Provided a series of examples,including direct inquiry method,source reliability assessment,identification of misleading statements,in depth analysis,citation and reference checks,and comprehensive evaluation,demonstrating how to evaluate the credibility of news.The results indicate that prompt engineering can effectively guide the model to complete specific tasks such as text classification,question answering,and text generation.A large scale language model combining RAG framework and prompt word engineering can significantly improve the accuracy and efficiency of fake news detection.