mlsanityData to 14 Oct 2026

GraphCheck: Multipath Fact-Checking with Entity-Relationship Graphs

arXiv 2502.20785 ↗First posted 28 Feb 2025cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

20 of 30 references.

References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  6. 2021A Survey on Automated Fact-Checkingcited by 378
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  9. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 201
  10. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  11. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  12. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
8 more
  1. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  2. 2019Reasoning Over Semantic-Level Graph for Fact Checkingcited by 69
  3. 2021Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrievalcited by 63
  4. 2020Multi-Hop Fact Checking of Political Claimscited by 37
  5. 2023EX-FEVER: A Dataset for Multi-hop Explainable Fact Verificationcited by 36
  6. 2019Text Infillingcited by 32
  7. 2023QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checkingcited by 26
  8. 2023Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphscited by 8

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Citing papers per year: 2025: 2.

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  1. 2025Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression
  2. 2024Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
  3. 2025GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
  4. 2025Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
  5. 2026KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking
  6. 2024CHECKWHY: Causal Fact Verification via Argument Structure
  7. 2025ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM
  8. 2025Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation