mlsanityData to 7 Oct 2026

Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models

arXiv 2402.12147 ↗First posted 19 Feb 2024cs.CL cs.AIcited by 14

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Abstract (arXiv)

In this paper, we explore the challenges associated with establishing an end-to-end fact-checking pipeline in a real-world context, covering over 90 languages. Our real-world experimental benchmarks demonstrate that fine-tuning Transformer models specifically for fact-checking tasks, such as claim detection and veracity prediction, provide superior performance over large language models (LLMs) like GPT-4, GPT-3.5-Turbo, and Mistral-7b. However, we illustrate that LLMs excel in generative tasks such as question decomposition for evidence retrieval. Through extensive evaluation, we show the efficacy of fine-tuned models for fact-checking in a multilingual setting and complex claims that include numerical quantities.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

13 of 22 references.

References · 13

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  4. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  5. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  6. 2021A Survey on Automated Fact-Checkingcited by 376
  7. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  8. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  9. 2020Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Societycited by 72
  10. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  11. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  12. 2020BRENDA: Browser Extension for Fake News Detectioncited by 20
1 more
  1. 2022ELASTIC: Numerical Reasoning with Adaptive Symbolic Compilercited by 14

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Citing papers per year: 2024: 3; 2025: 7; 2026: 4.

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  1. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  2. 2024Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?cited by 17
  3. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Modelscited by 13
  4. 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMscited by 5
  5. 2024QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applicationscited by 2
  6. 2025Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checkingcited by 2
  7. 2025Annotation Tool and Dataset for Fact-Checking Podcastscited by 2
  8. 2025Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verificationcited by 1
  9. 2025ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videoscited by 1
  10. 2025NumPert: Numerical Perturbations to Probe Language Models for Veracity Predictioncited by 1
  11. 2026One LLM to Train Them All: Multi-Task Learning Framework for Fact-Checkingcited by 1
  12. 2026Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMscited by 1
2 more
  1. 2026Calibrating Small Language Models for Claim Check-Worthiness Detectioncited by 0
  2. 2026Towards Robust Numerical Claim Verificationcited by 0