arXiv:2511.20233v4 Announce Type: replace Abstract: The prevalence of fake news on social media demands automated fact-checking systems to provide accurate verdicts with faithful explanations. However, existing large language model (LLM)-based approaches ignore deceptive misinformation styles in LLM-generated explanations, resulting in unfaithful rationales that can mislead human judgments. They rely heavily on external knowledge sources, introducing hallucinations and even high latency that undermine reliability and responsiveness, which is crucial for real-time use. To address these challeng
The rapid advancement of LLMs has brought to the forefront challenges of explainability and hallucination, making improved fact-checking systems a critical focus for maintaining trust in AI-generated information.
Reliable and explainable AI-driven fact-checking is crucial for combating misinformation, particularly as LLMs become more integrated into information ecosystems and decision-making processes.
This research introduces a self-refining, verdict-anchored approach to fact-checking, which could significantly improve the faithfulness and reliability of LLM explanations, moving beyond simple external knowledge reliance.
- · AI ethicists
- · Social media platforms
- · Fact-checking organizations
- · Generative AI developers focusing on trustworthiness
- · Malicious actors spreading misinformation
- · LLM-based systems prone to hallucination
- · Systems relying solely on external knowledge for fact-checking
Increased trust and adoption of AI-powered information verification systems.
Reduced spread of sophisticated misinformation, potentially impacting public discourse and political processes.
Development of regulatory frameworks and industry standards for explainable and reliable AI in information integrity.
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