推理路径即信号：通过结构化推理进展增强多跳事实验证
Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression
AI总结：
针对多跳事实验证中证据聚合与可解释推理不足的问题，提出将推理路径建模为结构化图的框架，并在 FEVER 和 HoVer 上验证其检索与验证性能提升。
摘要：
现实场景中事实声明日益复杂，给自动化事实验证系统带来了重大挑战，尤其是在准确聚合多跳证据并对其进行推理方面。现有方法通常依赖静态或浅层模型，无法捕捉推理路径不断演化的结构，导致检索碎片化且可解释性有限。为解决这些问题，我们提出了一种用于多跳事实验证的结构化推理框架，在证据检索和声明验证两个阶段都将推理路径显式建模为结构化图。该方法包含两个关键模块：一是结构增强的检索机制，通过构建推理图来指导证据收集；二是推理路径引导的验证模块，通过增量构建子图来表示不断演化的推理轨迹。我们还引入了结构感知推理机制，用于捕捉多跳证据链之间的长程依赖，从而实现更精确的验证。在 FEVER 和 HoVer 数据集上的大量实验表明，我们的方法始终优于强基线，凸显了推理路径建模在提升检索精度和验证准确率方面的有效性。
英文摘要：
The growing complexity of factual claims in real-world scenarios presents significant challenges for automated fact verification systems, particularly in accurately aggregating and reasoning over multi-hop evidence. Existing approaches often rely on static or shallow models that fail to capture the evolving structure of reasoning paths, leading to fragmented retrieval and limited interpretability. To address these issues, we propose a Structural Reasoning framework for Multi-hop Fact Verification that explicitly models reasoning paths as structured graphs throughout both evidence retrieval and claim verification stages. Our method comprises two key modules: a structure-enhanced retrieval mechanism that constructs reasoning graphs to guide evidence collection, and a reasoning-path-guided verification module that incrementally builds subgraphs to represent evolving inference trajectories. We further incorporate a structure-aware reasoning mechanism that captures long-range dependencies across multi-hop evidence chains, enabling more precise verification. Extensive experiments on the FEVER and HoVer datasets demonstrate that our approach consistently outperforms strong baselines, highlighting the effectiveness of reasoning-path modeling in enhancing retrieval precision and verification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
