ARAFA: An LLM Generated Arabic Fact Checking Dataset

Automatic fact-checking poses a significant challenge in Arabic natural language processing due to the scarcity of datasets and resources. In this manuscript, weintroduce ARAFA, a new large-scale dataset for fact-checking in Modern Standard Arabic, constructed through an automated framework leveraging large languagemodels (LLMs). The dataset was constructed through a three-step pipeline: (1) claim generation from Arabic Wikipedia pages with supporting textual evidence,(2) claim mutation to generate challenging counterfactual claims with refuting evidence, and (3) an automatic validation step to validate that the generated claimsare either supported or refuted by their accompanying evidence, or if the evidence does not provide enough information to judge the validity of the claims. Theresulting dataset comprises 181,976 claim-evidence pairs labeled as supported, refuted, or not enough information. Human evaluation carried out on a test sample from the dataset demonstrated strong inter-annotator agreement (κ = 0.89) using Cohen’s Kappa for supported claims and (κ = 0.94) for refuted claims.Automatic validation based on human-evaluated sample achieved 86% accuracy for supported claims and 88% for refuted ones. To showcase ARAFA’s value as aresource for automatic Arabic fact-checking, four open-source transformer-based models were fine-tuned using ARAFA, with the top-performing model achieving aMacro F1-score of 77% on the test data. In addition to ARAFA being the first large-scale dataset for Arabic fact-checking, our framework presents a scalableapproach for developing similar resources for other low-resource languages.