This research introduces a systematic approach to detect and address hallucinations in Large Language Models when queries contain false premises.

• Combines retrieval-augmented reasoning with logical verification to identify factual inconsistencies
• Uses a multi-step approach to analyze query premises before generating responses
• Demonstrated significant reduction in hallucination rates compared to baseline methods
• Achieves this without requiring access to model logits or expensive retraining

For security professionals, this work offers crucial improvements in ensuring LLMs deliver factually accurate information, reducing the risk of propagating misinformation or generating fabricated content in sensitive applications.

Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning