This research introduces a novel approach to reduce hallucination in large language models through smoothed knowledge distillation, addressing a critical challenge for deploying LLMs in high-stakes environments.

• Replaces traditional hard labels with smoothed probability distributions from teacher models
• Reduces model overconfidence and better represents uncertainty inherent in language
• Demonstrates improved factual accuracy and reduced hallucination rates
• Provides a practical training method that doesn't require extensive additional resources

From a security perspective, this advancement directly enhances reliability and trustworthiness of LLMs when deployed in sensitive contexts where factual accuracy is essential, reducing potential risks from AI-generated misinformation.

Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation