mlsanityData to 14 Oct 2026

Claim Verification in the Age of Large Language Models: A Survey

arXiv 2408.14317 ↗First posted 26 Aug 2024cs.CL cs.AIcited by 24

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Abstract (arXiv)

The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact verification has sparked the interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets created for this task.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 110 of its 153 references that are in the map. First posted on arXiv 26 Aug 2024.

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110 of 153 references.

References · 110

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38 more
  1. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
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  3. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 12; 2026: 11.

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  1. 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Expertscited by 43
  2. 2025Characterizing Deep Research: A Benchmark and Formal Definitioncited by 30
  3. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  4. 2025Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoningcited by 17
  5. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Modelscited by 13
  6. 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
  7. 2025Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)cited by 8
  8. 2026VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checkingcited by 6
  9. 2025Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representationcited by 5
  10. 2025Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluationcited by 5
  11. 2025When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verificationcited by 3
  12. 2025Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checkingcited by 2
12 more
  1. 2026Neurosymbolic Learning for Inference-Time Argumentationcited by 2
  2. 2025Bonsai: Interpretable Tree-Adaptive Grounded Reasoningcited by 1
  3. 2026M2-Verify: A Large-Scale Multidomain Benchmark for Checking Multimodal Claim Consistencycited by 1
  4. 2025Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosiscited by 0
  5. 2026From Symbolic to Natural-Language Relations: Rethinking Knowledge Graph Construction in the Era of Large Language Modelscited by 0
  6. 2026TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inferencecited by 0
  7. 2026MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessmentcited by 0
  8. 2026Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Modelscited by 0
  9. 2026One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QAcited by 0
  10. 2026CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QAcited by 0
  11. 2026Calibrating Small Language Models for Claim Check-Worthiness Detectioncited by 0
  12. 2026SoK: Formal Methods for Fact-Checking and Information Integritycited by 0

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  1. 2025Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning
  2. 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
  3. 2026DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification
  4. 2025Fact-checking AI-generated news reports: Can LLMs catch their own lies?
  5. 2026Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
  6. 2024IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection
  7. 2025ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM
  8. 2024Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability