mlsanityData to 7 Oct 2026

JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims

arXiv 2401.08026 ↗First posted 16 Jan 2024cs.CLcited by 29

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Abstract (arXiv)

Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for underlineExplainable fact-checking of real-world underlineClaims, and introduce JustiLM, a novel few-shot underlineJustification generation based on retrieval-augmented underlineLanguage underlineModel by using fact-check articles as auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 68 of its 89 references that are in the map. First posted on arXiv 16 Jan 2024.

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68 of 89 references.

References · 68

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Citing papers per year: 2024: 7; 2025: 13; 2026: 9.

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  1. 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27
  2. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  3. 2024Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLMcited by 14
  4. 2024Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategiescited by 10
  5. 2024Customized large language models can outperform Community Notes in correcting misinformationcited by 9
  6. 2025Entailed Between the Lines: Incorporating Implication into NLIcited by 9
  7. 2025Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detectioncited by 7
  8. 2025Can LLMs Automate Fact-Checking Article Writing?cited by 7
  9. 2024Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checkingcited by 5
  10. 2025CORRECT: Context- and Reference-Augmented Reasoning and Prompting for Fact-Checkingcited by 5
  11. 2025Adversarial Attacks Against Automated Fact-Checking: A Surveycited by 5
  12. 2024Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approachcited by 4
17 more
  1. 2025Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generatorcited by 4
  2. 2025Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verificationcited by 4
  3. 2026The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checkingcited by 3
  4. 2025GenAI vs. Human Fact-Checkers: Accurate Ratings, Flawed Rationalescited by 1
  5. 2025Explaining Sources of Uncertainty in Automated Fact-Checkingcited by 1
  6. 2025Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiquescited by 1
  7. 2025REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Controlcited by 1
  8. 2026Co-FactChecker: A Framework for Human-AI Collaborative Claim Verification Using Large Reasoning Modelscited by 1
  9. 2026From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidencecited by 1
  10. 2026Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformationcited by 1
  11. 2025EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detectioncited by 0
  12. 2025LLM-based Few-Shot Early Rumor Detection with Imitation Agentcited by 0
  13. 2026MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrievalcited by 0
  14. 2026CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchangescited by 0
  15. 2026SourceMinds at CheckThat! 2026: NLI-Grounded Citation Auditing in a Multi-Agent Pipeline for Full Fact-Checking Article Generationcited by 0
  16. 2026UTS at CheckThat! 2026: Cite-Frame Engineering for Generated Fact-Checking Articlescited by 0
  17. 2026CoVer: Conflict-Aware Claim Verificationcited by 0

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