SoK: Formal Methods for Fact-Checking and Information Integrity
Not on the map. Why?
Abstract (arXiv)
An automated fact-checking system returns a label: the claim is true, or it is false. In many such systems the verdict remains the primary output. What is generally missing is a record of which document settled the question, of what would have had to be different for the verdict to change, or of whether the same claim, reworded, would have been judged the same way. We call the missing piece a warrant: a separate statement of what was guaranteed and on what grounds. Formal methods produce evidence of this kind, and regulation is beginning to ask for it, since the Digital Services Act and the AI Act both call for auditable evidence about how systems behave. Surveys of automated fact-checking are usually organised by pipeline stage, and treat logic as one technique among many. We organise the field by what is being formalised instead, which gives five levels: the claim, the reasoning, the system doing the checking, the ecosystem the claim spreads through, and the regulatory obligation. Sorting 121 works into those levels, two patterns stand out. Most of the relevant formal machinery already exists, but it was built for other domains and has rarely been applied here, and the gap is widest for verifying the checking system itself. Several stages of the routine professional fact-checkers follow also have no stated correctness criterion, and two of them, writing a claim in checkable form and correcting a verdict already published, are not formally specified in any work we coded. We close with open problems, each with a suggested first step.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 88 of its 152 references that are in the map. First posted on arXiv 19 Sep 2026.
Builds on
88 of 152 references.
References · 88
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
- 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 739
- 2020Fact or Fiction: Verifying Scientific Claimscited by 546
- 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
- 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
- 2021A Survey on Automated Fact-Checkingcited by 376
- 2022Conformal Risk Controlcited by 290
- 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 224
- 2020Explainable Automated Fact-Checking for Public Health Claimscited by 209
- 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 199
- 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
38 more
- 2019Certified Robustness to Adversarial Word Substitutionscited by 164
- 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
- 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
- 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
- 2024Language Models with Conformal Factuality Guaranteescited by 128
- 2019Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagationcited by 87
- 2020Explainable Automated Fact-Checking: A Surveycited by 83
- 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
- 2020SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutionscited by 72
- 2015Hinge-Loss Markov Random Fields and Probabilistic Soft Logiccited by 63
- 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
- 2021Automated Fact-Checking: A Surveycited by 55
- 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 52
- 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
- 2022Synthetic Disinformation Attacks on Automated Fact Verification Systemscited by 44
- 2015A Survey on Truth Discoverycited by 36
- 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
- 2017A storm is Coming: A Modern Probabilistic Model Checkercited by 34
- 2024Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?cited by 34
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
- 2024OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMscited by 28
- 2024Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Toolingcited by 27
- 2020LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Networkcited by 26
- 2025The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Resultscited by 26
- 2015Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphscited by 23
- 2019Explainable Fact Checking with Probabilistic Answer Set Programmingcited by 23
- 2024Claim Verification in the Age of Large Language Models: A Surveycited by 23
- 2024QuanTemp: A real-world open-domain benchmark for fact-checking numerical claimscited by 22
- 2025Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Modelscited by 20
- 2024Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detectioncited by 18
- 2017Finding Streams in Knowledge Graphs to Support Fact Checkingcited by 17
- 2022Human and technological infrastructures of fact-checkingcited by 16
- 2023Interpretable Multimodal Misinformation Detection with Logic Reasoningcited by 16
- 2024Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approachescited by 15
- 2022QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networkscited by 12
- 2023QA-NatVer: Question Answering for Natural Logic-based Fact Verificationcited by 8
- 2024Towards a Framework for Evaluating Explanations in Automated Fact Verificationcited by 7
- 2025Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Provingcited by 6
50 most cited of 88 All 88 (JSON)
Built on by
Not cited yet.