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Benchmarking Check-Worthiness Models on LLM Generated Claims

The proliferation of large language models (LLMs) has significantly increased the potential for automated dissemination of disinformation, necessitating robust systems for check-worthiness detection. However, existing models are primarily trained on human claims, leaving their performance on machine-generated text largely unexplored. In this paper, we benchmark encoder models (BERT and RoBERTa) and industry accessible tools (ClaimBuster) against LLM-paraphrased claims across three stylistic categories: syntactic restructuring, syntactic complexity and lexical informality. Our results indicate a consistent performance degradation on synthetic claims, particularly on complex and informal claims. We demonstrate that adversarial training significantly improves model resilience, with RoBERTa achieving F1-score gains up to +5.22 on the CheckIt dataset. Finally, SHAP analysis reveals that while base models rely on narrow syntactic heuristics such as active voice, robust models learn to anchor their prediction on core factual entities. These findings highlight the necessity of stylistic-aware training to maintain fact-checking efficacy in an increasingly LLM-populated information landscape.