This research introduces a reliable, high-speed system for detecting and correcting hallucinations in large language models, making AI applications more trustworthy.

• Combines multiple detection techniques including named entity recognition, natural language inference, and span-based detection
• Implements a sophisticated decision framework to identify and address different types of hallucinations
• Offers both detection and mitigation capabilities in a production environment
• Prioritizes speed and reliability for practical business implementation

From a security perspective, this system prevents the spread of factually incorrect information generated by LLMs, significantly reducing risks in critical applications and enhancing overall system trustworthiness.

Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service