🤖 AI Summary
To address fragmented evidence aggregation, insufficient modeling of reasoning paths, and limited interpretability in multi-hop fact verification, this paper proposes a structured reasoning framework. Methodologically, it explicitly models the dynamically evolving reasoning path as a subgraph structure spanning both retrieval and verification stages; introduces a synergistic dual-module architecture—structure-enhanced retrieval and path-guided verification; and incorporates structure-aware long-range dependency modeling. The approach integrates graph neural networks, incremental subgraph construction, and joint retrieval-verification optimization. Evaluated on FEVER and HoVer benchmarks, it significantly outperforms strong baselines, achieving simultaneous gains in verification accuracy and retrieval precision. Moreover, it improves reasoning consistency and enhances interpretability through transparent, graph-structured inference pathways.
📝 Abstract
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.
