mlsanityData to 7 Oct 2026

Task-Oriented Automatic Fact-Checking with Frame-Semantics

arXiv 2501.13288 ↗First posted 23 Jan 2025cs.CLcited by 3

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Abstract (arXiv)

We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future work in this direction.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 72 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

22 of 72 references.

References · 22

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  4. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  5. 2021Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Samplingcited by 238
  6. 2022Binding Language Models in Symbolic Languagescited by 224
  7. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 223
  8. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  9. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  10. 2024Jasper and Stella: distillation of SOTA embedding modelscited by 109
  11. 2018Semantic Parsing for Task Oriented Dialog using Hierarchical Representationscited by 101
  12. 2020INFOTABS: Inference on Tables as Semi-structured Datacited by 84
10 more
  1. 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
  2. 2022PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingcited by 41
  3. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  4. 2023FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checkingcited by 37
  5. 2021SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)cited by 35
  6. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  7. 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27
  8. 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 23
  9. 2024OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMscited by 13
  10. 2023Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labelscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026ClaimDB: A Fact Verification Benchmark over Large Structured Datacited by 3
  2. 2026CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrievalcited by 1
  3. 2026Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Datacited by 0