In the current digital era, the rapid spread of misinformation on online platforms presents significant challenges to societal well-being, public trust, and democratic processes, influencing critical decision making and public opinion. To address these challenges, there is a growing need for automated fake news detection mechanisms. Pre-trained large language models (LLMs) have demonstrated exceptional capabilities across various natural language processing (NLP) tasks, prompting exploration into their potential for verifying news claims. Instead of employing LLMs in a non-agentic way, where LLMs generate responses based on direct prompts in a single shot, our work introduces FactAgent, an agentic approach of utilizing LLMs for fake news detection. FactAgent enables LLMs to emulate human expert behavior in verifying news claims without any model training, following a structured workflow. This workflow breaks down the complex task of news veracity checking into multiple sub-steps, where LLMs complete simple tasks using their internal knowledge or external tools. At the final step of the workflow, LLMs integrate all findings throughout the workflow to determine the news claim's veracity. Compared to manual human verification, FactAgent offers enhanced efficiency. Experimental studies demonstrate the effectiveness of FactAgent in verifying claims without the need for any training process. Moreover, FactAgent provides transparent explanations at each step of the workflow and during final decision-making, offering insights into the reasoning process of fake news detection for end users. FactAgent is highly adaptable, allowing for straightforward updates to its tools that LLMs can leverage within the workflow, as well as updates to the workflow itself using domain knowledge. This adaptability enables FactAgent's application to news verification across various domains.

翻译：在当今数字时代，虚假信息在网络平台上的快速传播对社会福祉、公众信任和民主进程构成了重大挑战，影响着关键决策和公众舆论。为应对这些挑战，自动化假新闻检测机制的需求日益增长。预训练大语言模型在各种自然语言处理任务中展现出卓越能力，这促使研究者探索其在新闻真实性验证方面的潜力。不同于以非智能体方式（即LLM基于单次直接提示生成响应）应用LLM，我们的工作提出了FactAgent——一种利用LLM进行假新闻检测的智能体方法。FactAgent使LLM能够模拟人类专家在验证新闻声明时的行为，无需任何模型训练即可遵循结构化工作流。该工作流将新闻真实性验证这一复杂任务分解为多个子步骤，LLM通过内部知识或外部工具完成简单任务。在工作流最终步骤中，LLM整合流程中所有发现结果以判定新闻声明的真实性。相较于人工验证，FactAgent具有更高的效率。实验研究证明，FactAgent无需任何训练过程即可有效验证新闻声明。此外，FactAgent在工作流各步骤及最终决策过程中提供透明的解释，为终端用户揭示假新闻检测的推理过程。FactAgent具有高度适应性，既可便捷更新其工作流中LLM可调用的工具，也可根据领域知识直接更新工作流本身。这种适应性使FactAgent能够应用于不同领域的新闻验证任务。