Papers with fact-checking studies

2 papers
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)

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Challenge: Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs.
Approach: They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction.
Outcome: Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis.
Explainable Automated Fact-Checking for Public Health Claims (2020.emnlp-main)

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Challenge: a few blind spots exist in the state-of-the-art in fact-checking for political claims.
Approach: They propose to use a dataset of 11.8K claims to explain fact-check labels for claims . they define and evaluate three coherence properties of explanation quality with humans .
Outcome: The proposed model can be trained on in-domain data and evaluates its coherence properties with humans and computationally.

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