This research introduces a novel technique for detecting hallucinations in Large Language Models by strategically injecting noise into model inputs to magnify uncertainty signals.

• Noise injection significantly improves hallucination detection accuracy compared to standard sampling methods
• Researchers found that different noise types and magnitudes affect detection performance differently
• The method is model-agnostic and can be applied to various LLM architectures
• The approach requires no additional training and can be implemented with minimal computational overhead

For security teams, this research provides a practical way to enhance LLM reliability in production systems, reducing the risk of false or harmful information being presented to users as factual content.

Enhancing Hallucination Detection through Noise Injection