通过迭代拆解复杂陈述为原子事实，提升谣言验证的准确性和可解释性。
Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification
- 用大模型动态拆分复杂陈述为原子事实，逐步优化理解
- 在五个数据集上达到顶尖准确率，误判率显著降低
- 适合需要高精度推理的虚假信息检测场景
事实验证在对抗虚假信息中至关重要，需通过证据检索与推理评估陈述真伪。然而，传统方法在处理需多跳推理的复杂陈述时表现不佳，因其依赖静态分解策略和表层语义检索，难以捕捉陈述的细微结构与意图，导致推理错误累积、证据噪声污染，并缺乏对多样化陈述的适应性，最终削弱复杂场景下的验证精度。为此，我们提出原子事实提取与验证（AFEV）框架，通过迭代方式将复杂陈述分解为原子事实，实现细粒度检索与自适应推理。AFEV通过迭代提取动态优化陈述理解，重排证据以过滤噪声，并利用上下文相关示例引导推理过程。在五个基准数据集上的大量实验表明，AFEV在准确率与可解释性方面均达到当前最优水平。
原文摘要 · Abstract (English)
Fact verification plays a vital role in combating misinformation by assessing the veracity of claims through evidence retrieval and reasoning. However, traditional methods struggle with complex claims requiring multi-hop reasoning over fragmented evidence, as they often rely on static decomposition strategies and surface-level semantic retrieval, which fail to capture the nuanced structure and intent of the claim. This results in accumulated reasoning errors, noisy evidence contamination, and limited adaptability to diverse claims, ultimately undermining verification accuracy in complex scenarios. To address this, we propose Atomic Fact Extraction and Verification (AFEV), a novel framework that iteratively decomposes complex claims into atomic facts, enabling fine-grained retrieval and adaptive reasoning. AFEV dynamically refines claim understanding and reduces error propagation through iterative fact extraction, reranks evidence to filter noise, and leverages context-specific demonstrations to guide the reasoning process. Extensive experiments on five benchmark datasets demonstrate that AFEV achieves state-of-the-art performance in both accuracy and interpretability.
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