mlsanityData to 14 Oct 2026

Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation

arXiv 2505.22993 ↗First posted 29 May 2025cs.CL cs.AI cs.DBcited by 5

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Abstract (arXiv)

Claim verification is a long-standing and challenging task that demands not only high accuracy but also explainability of the verification process. This task becomes an emerging research issue in the era of large language models (LLMs) since real-world claims are often complex, featuring intricate semantic structures or obfuscated entities. Traditional approaches typically address this by decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. However, the absence of effective disambiguation strategies for these entities can compromise the entire verification process. To address these challenges, we propose Verify-in-the-Graph (VeGraph), a novel framework leveraging the reasoning and comprehension abilities of LLM agents. VeGraph operates in three phases: (1) Graph Representation - an input claim is decomposed into structured triplets, forming a graph-based representation that integrates both structured and unstructured information; (2) Entity Disambiguation -VeGraph iteratively interacts with the knowledge base to resolve ambiguous entities within the graph for deeper sub-claim verification; and (3) Verification - remaining triplets are verified to complete the fact-checking process. Experiments using Meta-Llama-3-70B (instruct version) show that VeGraph achieves competitive performance compared to baselines on two benchmarks HoVer and FEVEROUS, effectively addressing claim verification challenges. Our source code and data are available for further exploitation.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

10 of 15 references.

References · 10

  1. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  2. 2021A Survey on Automated Fact-Checkingcited by 378
  3. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 201
  4. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
  5. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  6. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  7. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  8. 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 53
  9. 2024Claim Verification in the Age of Large Language Models: A Surveycited by 24
  10. 2024To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguitycited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

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  1. 2025ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM
  2. 2024Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph
  3. 2026VeriGraph: Towards Verifiable Data-Analytic Agents
  4. 2025GraphCheck: Multipath Fact-Checking with Entity-Relationship Graphs
  5. 2026Benchmarking Large Language Models for Knowledge Graph Validation
  6. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
  7. 2025Graph of Verification: Structured Verification of LLM Reasoning with Directed Acyclic Graphs
  8. 2025Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification