G raph C heck: Multipath Fact-Checking with Entity-Relationship Graphs (2025)

Abstract: Automated fact-checking aims to assess the truthfulness of textual claims based on relevant evidence. However, verifying complex claims that require multi-hop reasoning remains a significant challenge. We propose GraphCheck , a novel framework that transforms claims into entity-relationship graphs for structured and systematic fact-checking. By explicitly modeling both explicit and latent entities and exploring multiple reasoning paths, GraphCheck enhances verification robustness. While GraphCheck excels in complex scenarios, it may be unnecessarily elaborate for simpler claims. To address this, we introduce DP-GraphCheck , a variant that employs a lightweight strategy selector to choose between direct prompting and GraphCheck adaptively. This selective mechanism improves both accuracy and efficiency by applying the appropriate level of reasoning to each claim. Experiments on the HOVER and EX-FEVER datasets demonstrate that our approach outperforms existing methods in verification accuracy, while achieving strong computational efficiency despite its multipath exploration. Moreover, the strategy selection mechanism in DP-GraphCheck generalizes well to other fact-checking pipelines, highlighting the broad applicability of our framework.

## introduction automated fact-checking is a task that assesses the truthfulness of claims based on relevant evidence. with a standard pipeline that includes claim detection, evidence retrieval, and veracity assessment, automated systems enhance efficiency and accuracy in fact-checking @xcite . however, verifying complex claims that require multi-hop reasoning remains a significant challenge. such claims often consist of interwoven subclaims, making them difficult to verify at once. also, relevant the overall process consists of three steps: (1) a claim is converted into a structured entity-relationship graph in which both explicit and latent entities are represented. (2) latent entities are then identified through text infilling. multiple infilling paths are explored via graph traversal, resulting in multiple infilled graphs. (3) for each infilled graph, every triplet is individually verified. the claim is predicted as supported if at least one path yields a graph in which all triplets are verified as supported; otherwise, the claim is predicted as not supported. to address these limitations, we propose graphcheck, in which llms transform textbased claims into structured entity-relationship graphs through which diverse verification paths can be generated. these graphs consist of fact triplets, each defining a relationship between entities and serving as an independently verifiable subclaim. compared to prior approaches that extract check-worthy components without a clear structure, graphcheck effectively performs fine-grained claim decomposition while preserving key components, enabling more comprehensive verification. furthermore, by enabling flexible graph traversal, graphcheck avoids reliance on a single reasoning path and allows multiple orders of latent entity identification. this flexibility increases the likelihood of capturing an optimal reasoning path, improving the robustness of verification. while graphcheck thoroughly verifies complex claims, it may be unnecessarily elaborate for simpler cases. some relatively simple claims can be verified more efficiently and effectively through direct prompting (dp or direct), where the llm directly assesses a claim's truthfulness based on relevant documents. to leverage the complementary strengths of direct and graphcheck, we introduce dp-graphcheck, which employs a lightweight strategy selector to choose between the two methods adaptively. notably, this strategy selector is modular and can be easily integrated into diverse fact-checking systems beyond graphcheck. experimental results on multi-hop fact-checking datasets @xcite demonstrate that both graphcheck and dp-graphcheck outperform existing methods on the complex multi-hop fact-checking task, highlighting the effectiveness of structuring claims as entityrelationship graphs. despite its multipath exploration, graphcheck achieves superior computational efficiency compared to multipath baselines. moreover, the strategy selector improves performance not only within graphcheck but also across other baseline fact-checking systems, underscoring its broad applicability. early approaches to multi-hop fact-checking rely on task-specific supervised training of neural models with annotated datasets @xcite @xcite . while these methods demonstrate strong in-domain performance and maintain computational efficiency during inference through lightweight architectures, they are fundamentally limited by the high cost of large-scale annotation and poor cross-domain generalization. the advent of large language models (llms) has shifted the field toward more flexible, contextaware reasoning @xcite . llm-based approaches often combine retrieval-augmented generation (rag) @xcite with in-context learning @xcite to support veracity prediction without additional task-specific training. recent work has explored various strategies, such as iterative rag @xcite and claim decomposition @xcite @xcite . building on these advances, graphcheck introduces a graph-based decomposition approach that structures claims into entities and relations, enabling more comprehensive multi-hop fact-checking. ## conclusion we introduce graphcheck, a novel framework for automated fact-checking based on entityrelationship graphs, specifically designed to handle multi-hop reasoning. by converting claims into structured entity-relationship graphs and exploring multiple identification paths, our approach enables comprehensive and systematic fact-checking. additionally, we proposed dp-graphcheck, which leverages a lightweight strategy selector to adaptively choose between simple direct prompting and more systematic graph-based fact-checking, thereby enhancing both efficiency and robustness. our experiments on the hover and ex-fever datasets demonstrate that dp-graphcheck consistently achieves superior performance across multiple settings, while maintaining high computational efficiency. furthermore, the strategy selector enhances the performance of other fact-checking methods, highlighting its broad applicability. ablation studies validate the effectiveness of each component, including multipath exploration and document-level verification strategies. in addition, we confirm that graphcheck generalizes across various graph construction models, including both alternative llms and fine-tuned smaller models. overall, our findings highlight graphcheck and dp-graphcheck as strong and extensible frameworks for multi-hop fact-checking.