With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-checkers to manually verify the sheer volume of information generated online. Professional fact-checkers have identified several gaps in existing AFC systems, noting a misalignment between how these systems operate and how fact-checking is performed in practice. In this paper, we introduce CAAFC (Chronological Actionable Automated Fact-Checker), a frame-work designed to bridge these gaps. It surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets. CAAFC operates on claims, conversations, and dialogues, enabling it not only to detect factual errors and hallucinations, but also to correct them by providing actionable justifications supported by primary information sources. Furthermore, CAAFC can update evidence and knowledge bases by incorporating recent and contextual information when necessary, thereby enhancing the reliability of fact verification.

翻译：随着每小时上传的海量内容以及可能包含幻觉的AI生成内容，自动事实核查（AFC）变得日益关键，因为人工事实核查员已无法手动验证线上产生的庞大数据量。专业事实核查员指出当前AFC系统存在若干空白，揭示出这些系统的运作方式与实际事实核查流程之间的错位。本文提出CAAFC（时间顺序可操作自动事实核查器）框架以弥合上述差距。该框架在多个基准数据集上超越当前最先进的AFC与幻觉检测系统。CAAFC可处理声明、对话及多轮交互，不仅能检测事实错误与幻觉，还能通过提供以主要信息来源为支撑的可操作依据进行纠错。此外，CAAFC能在必要时纳入近期与上下文信息更新证据与知识库，从而提升事实核查的可靠性。