mlsanityData to 13 Oct 2026

Do We Need Language-Specific Fact-Checking Models? The Case of Chinese

arXiv 2401.15498 ↗First posted 27 Jan 2024cs.CLcited by 15

Not on the map. Why?

Abstract (arXiv)

This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese. We first demonstrate the limitations of translation-based methods and multilingual large language models (e.g., GPT-4), highlighting the need for language-specific systems. We further propose a Chinese fact-checking system that can better retrieve evidence from a document by incorporating context information. To better analyze token-level biases in different systems, we construct an adversarial dataset based on the CHEF dataset, where each instance has large word overlap with the original one but holds the opposite veracity label. Experimental results on the CHEF dataset and our adversarial dataset show that our proposed method outperforms translation-based methods and multilingual LLMs and is more robust toward biases, while there is still large room for improvement, emphasizing the importance of language-specific fact-checking systems.

Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 31 of its 55 references that are in the map. First posted on arXiv 27 Jan 2024.

Builds on

31 of 55 references.

References · 31

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  10. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
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19 more
  1. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 200
  2. 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
  3. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
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  10. 2018The Fact Extraction and VERification (FEVER) Shared Taskcited by 108
  11. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  12. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 91
  13. 2020FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19cited by 60
  14. 2023Multimodal Automated Fact-Checking: A Surveycited by 52
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 9; 2025: 5; 2026: 1.

15 papers.

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  1. 2024LUQ: Long-text Uncertainty Quantification for LLMscited by 81
  2. 2024Conformity in Large Language Modelscited by 32
  3. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  4. 2024LongGenBench: Long-context Generation Benchmarkcited by 28
  5. 2024Atomic Calibration of LLMs in Long-Form Generationscited by 24
  6. 2024Can Large Language Models Generate High-quality Patent Claims?cited by 19
  7. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  8. 2024LoGU: Long-form Generation with Uncertainty Expressionscited by 19
  9. 2026Confidence Estimation for LLMs in Multi-turn Interactionscited by 12
  10. 2025Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Modelscited by 10
  11. 2024Evaluating Robustness of Generative Search Engine on Adversarial Factual Questionscited by 9
  12. 2025FaStfact: Faster, Stronger Long-Form Factuality Evaluations in LLMscited by 8
3 more
  1. 2025Adversarial Attacks Against Automated Fact-Checking: A Surveycited by 5
  2. 2024A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verificationcited by 1
  3. 2025PledgeTracker: A System for Monitoring the Fulfilment of Pledgescited by 0