SINdex introduces a scalable framework that uses semantic clustering to automatically detect hallucinations in large language models, requiring no external data or model modifications.

Key Innovations:

• Leverages semantic inconsistency patterns across multiple model outputs to identify hallucinations
• Provides a model-agnostic solution compatible with any standard LLM
• Offers easy implementation without requiring external knowledge bases
• Helps prevent security risks from AI-generated misinformation

This research addresses critical security concerns when deploying LLMs across domains like healthcare, legal, and finance where factual accuracy is essential for maintaining trust and preventing harmful decisions based on hallucinated content.

SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs