mlsanityData to 14 Oct 2026

Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

arXiv 2310.05253 ↗First posted 8 Oct 2023cs.CL cs.AI cs.LGcited by 37

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Claim verification plays a crucial role in combating misinformation. While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale. Additionally, it is important for models to provide comprehensive explanations that can justify their decisions and assist human fact-checkers. This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK) Reasoning that can verify complex claims and generate explanations without the need for annotated evidence using Large Language Models (LLMs). FOLK leverages the in-context learning ability of LLMs to translate the claim into a First-Order-Logic (FOL) clause consisting of predicates, each corresponding to a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning over a set of knowledge-grounded question-and-answer pairs to make veracity predictions and generate explanations to justify its decision-making process. This process makes our model highly explanatory, providing clear explanations of its reasoning process in human-readable form. Our experiment results indicate that FOLK outperforms strong baselines on three datasets encompassing various claim verification challenges. Our code and data are available.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 46 of its 76 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

46 of 76 references.

Closest ancestors · 10

Same direction, newest first

  1. 2023Fact-Checking Complex Claims with Program-Guided Reasoning22 May
  2. 2023Natural Language Reasoning, A Survey26 Mar
  3. 2023LLaMA: Open and Efficient Foundation Language Models27 Feb
  4. 2022LAMBADA: Backward Chaining for Automated Reasoning in Natural Language20 Dec
  5. 2022PAL: Program-aided Language Models18 Nov
  6. 2022Measuring and Narrowing the Compositionality Gap in Language Models7 Oct
  7. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct
  8. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning19 May
  9. 2022FaiRR: Faithful and Robust Deductive Reasoning over Natural Language19 Mar
  10. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Models30 Nov

Other references · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  8. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  9. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  11. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
  12. 2021A Survey on Automated Fact-Checkingcited by 378
24 more
  1. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 364
  2. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 353
  3. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
  4. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 201
  5. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
  6. 2020Generating Fact Checking Explanationscited by 132
  7. 2023Solving Math Word Problems by Combining Language Models With Symbolic Solverscited by 124
  8. 2019Multi-hop Reading Comprehension through Question Decomposition and Rescoringcited by 119
  9. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  10. 2018DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learningcited by 100
  11. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
  12. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  13. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87
  14. 2020Explainable Automated Fact-Checking: A Surveycited by 83
  15. 2019Reasoning Over Semantic-Level Graph for Fact Checkingcited by 69
  16. 2022Multi-hop Question Answeringcited by 64
  17. 2021Towards Few-Shot Fact-Checking via Perplexitycited by 63
  18. 2022Generating Scientific Claims for Zero-Shot Scientific Fact Checkingcited by 59
  19. 2021Zero-shot Fact Verification by Claim Generationcited by 42
  20. 2021Towards Fine-Grained Reasoning for Fake News Detectioncited by 32
  21. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  22. 2023Predicting Information Pathways Across Online Communitiescited by 15
  23. 2022Fact Checking with Insufficient Evidencecited by 11
  24. 2023MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapterscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 10; 2025: 17; 2026: 10.

37 papers.

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  1. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  2. 2024Argumentative Large Language Models for Explainable and Contestable Claim Verificationcited by 36
  3. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Argumentscited by 25
  4. 2024Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agendacited by 22
  5. 2024Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verificationcited by 17
  6. 2024Web Retrieval Agents for Evidence-Based Misinformation Detectioncited by 13
  7. 2024Piecing It All Together: Verifying Multi-Hop Multimodal Claimscited by 12
  8. 2025ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checkingcited by 10
  9. 2025Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)cited by 8
  10. 2025Reasoning-CV: Fine-tuning Powerful Reasoning LLMs for Knowledge-Assisted Claim Verificationcited by 6
  11. 2025CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detectioncited by 6
  12. 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMscited by 5
25 more
  1. 2025MPCG: Multi-Round Persona-Conditioned Generation for Modeling the Evolution of Misinformation with LLMscited by 4
  2. 2024TabVer: Tabular Fact Verification with Natural Logiccited by 3
  3. 2025STRIVE: Structured Reasoning for Self-Improvement in Claim Verificationcited by 3
  4. 2026Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checkingcited by 3
  5. 2024Context Graphcited by 2
  6. 2025Evidence-Grounded Multimodal Misinformation Detection with Attention-Based GNNscited by 2
  7. 2025Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verificationcited by 2
  8. 2025IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Datacited by 2
  9. 2025CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMscited by 1
  10. 2025Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiquescited by 1
  11. 2025LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verificationcited by 1
  12. 2025Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglementcited by 1
  13. 2026Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMscited by 1
  14. 2024Fact-checking based fake news detection: a reviewcited by 0
  15. 2024Mining the Explainability and Generalization: Fact Verification Based on Self-Instructioncited by 0
  16. 2025Bridging Natural Language and ASP: A Hybrid Approach Using LLMs and AMR Parsingcited by 0
  17. 2025Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosiscited by 0
  18. 2026Fact-Checking with Large Language Models via Probabilistic Certainty and Consistencycited by 0
  19. 2026Global Consistency Repair for Natural-Language Claim Setscited by 0
  20. 2026Retrieve-Refine-Calibrate: A Framework for Complex Claim Fact-Checkingcited by 0
  21. 2026MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessmentcited by 0
  22. 2026The Alignment Bottleneck in Decomposition-Based Claim Verificationcited by 0
  23. 2026A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLMcited by 0
  24. 2026Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Modelscited by 0
  25. 2026Attribute-Based Activation Steering of LLMs for Group-Specific Explanation Generationcited by 0

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  1. 2026Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
  2. 2026ART: Adaptive Reasoning Trees for Explainable Claim Verification
  3. 2023Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
  4. 2026ThinknCheck: Grounded Claim Verification with Compact, Reasoning-Driven, and Interpretable Models
  5. 2026Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation
  6. 2024Robust Claim Verification Through Fact Detection
  7. 2024Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification
  8. 2025Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification