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.

在当前数字化时代，社交平台上虚假信息的快速传播对社会福祉、公众信任和民主进程产生了重大挑战，影响着重要决策和公众舆论。为了应对这些挑战，自动化的虚假新闻检测机制的需求日益增长。本研究引入了 FactAgent，一种以主体性方式利用大型语言模型 (LLM) 进行虚假新闻检测的方法，将复杂的新闻真实性检查任务拆分为多个子步骤，LLM 可以使用内部知识或外部工具完成简单任务。FactAgent 在提高效率的同时，还提供透明的解释以及对虚假新闻检测推理过程的见解，具有高度的适应性，可以适用于各个领域的新闻验证。